Category: Taxation

  • MIL-OSI Security: Former Arkansas State Senator Sentenced for Role in Bribery Scheme

    Source: US FBI

    A former Arkansas state senator was sentenced yesterday to four years and two months in prison in the Western District of Missouri for accepting multiple bribes in connection with a multi-district investigation spanning the Eastern and Western Districts of Arkansas and the Western District of Missouri.

    Pursuant to his global plea agreement, Jeremy Hutchinson, 48, of Little Rock, pleaded guilty on June 25, 2019, in the Eastern District of Arkansas to filing a false tax return; pleaded guilty on June 25, 2019, to an information filed in the Western District of Arkansas to conspiracy to commit federal program bribery; and pleaded guilty in the Western District of Missouri on July 8, 2019, to conspiracy to commit federal program bribery. On Feb. 3, Hutchison was sentenced to three years and 10 months in prison for his convictions in the Eastern District of Arkansas and Western District of Arkansas. His sentence in the Western District of Missouri will run consecutive to the previous sentence for a total of eight years in prison.

    According to court documents in connection with his plea in the Western District of Missouri, Hutchinson was hired by then-chief operating officer Bontiea Goss as outside counsel for Preferred Family Healthcare Inc. (formerly known as Alternative Opportunities Inc.), a Springfield, Missouri-based healthcare charity. In exchange for payments and legal work, Hutchinson performed official acts on behalf of Preferred Family Healthcare, including holding up agency budgets and drafting and voting on legislation. Preferred Family Healthcare paid Hutchinson more than $350,000 in monthly retainer payments from May 2014 until 2017.

    In 2022, Preferred Family Healthcare agreed to pay more than $8 million in forfeiture and restitution to the federal government and the state of Arkansas under the terms of a non-prosecution agreement, in which the company admitted to the criminal conduct of its former officers and employees.

    Several former executives from the charity, former members of the Arkansas state legislature, and others have pleaded guilty in federal court as part of the long-running, multi-jurisdiction investigation, including the following:

    • Former Chief Operating Officer Bontiea Goss, previously of Springfield, Missouri, pleaded guilty in September 2022 to her role in a conspiracy to commit bribery concerning programs receiving federal funds.
    • Former Chief Financial Officer Tommy “Tom” Ray Goss, husband of Bontiea Goss, and also previously of Springfield, Missouri, pleaded guilty in September 2022 to participating in the conspiracy by embezzling funds from the charity, as well as by paying bribes and kickbacks to elected public officials in Arkansas. Tom Goss also pleaded guilty to one count of aiding and assisting in the preparation and presentation of a false tax return.
    • Former Chief Executive Officer Marilyn Luann Nolan of Springfield, Missouri, pleaded guilty in November 2018 to her role in a conspiracy to embezzle and misapply the funds of a charitable organization that received federal funds.
    • Former Director of Operations and Executive Vice President Robin Raveendran of Little Rock, Arkansas, pleaded guilty in June 2019 to conspiracy to commit bribery concerning programs receiving federal funds.
    • Former executive and head of clinical operations Keith Fraser Noble of Rogersville, Missouri, pleaded guilty in September 2019 to concealment of a known felony.
    • Former employee and head of operations and lobbying in Arkansas Milton Russell Cranford, aka Rusty, of Rogers, Arkansas, was sentenced to seven years in federal prison after pleading guilty to one count of federal program bribery.
    • Political consultant Donald Andrew Jones, aka D.A. Jones, of Willingboro, New Jersey, pleaded guilty in December 2017 to his role in a conspiracy to steal from an organization that receives federal funds.
    • Former Arkansas State Representative Eddie Wayne Cooper of Melbourne, Arkansas, pleaded guilty in February 2018 to conspiracy to embezzle more than $4 million from Preferred Family Healthcare.
    • Former Arkansas State Senator and State Representative Henry “Hank” Wilkins IV was sentenced in January 2023 for his role in a conspiracy to commit federal program bribery and devising a scheme and artifice to defraud and deprive the citizens of the state of Arkansas of their right to honest services.

    Assistant Attorney General Kenneth A. Polite, Jr. of the Justice Department’s Criminal Division, U.S. Attorney Jonathan D. Ross for the Eastern District of Arkansas, U.S. Attorney David Clay Fowlkes for the Western District of Arkansas, U.S. Attorney Teresa A. Moore for the Western District of Missouri, Assistant Director Luis Quesada of the FBI’s Criminal Investigative Division, Special Agent in Charge Charles Dayoub of the FBI Kansas City Field Office, Special Agent in Charge James A. Dawson of the FBI Little Rock Field Office, and Acting Special Agent in Charge Thomas F. Murdock of the IRS Criminal Investigation (IRS-CI) St. Louis Field Office made the announcement.

    The FBI, IRS-CI, the Offices of the Inspectors General from the Departments of Justice, Labor, and the Federal Deposit Insurance Corporation investigated the cases.

    Senior Litigation Counsel Marco A. Palmieri, Director of Enforcement & Litigation for the Election Crimes Branch Sean F. Mulryne, and Trial Attorney Jacob Steiner of the Criminal Division’s Public Integrity Section; Assistant U.S. Attorney Stephanie Mazzanti for the Eastern District of Arkansas; Supervisory Assistant U.S. Attorney Randall Eggert and Assistant U.S. Attorney Shannon T. Kempf for the Western District of Missouri; and Assistant U.S. Attorneys Aaron L. Jennen and Steven M. Mohlhenrich for the Western District of Arkansas are prosecuting the separate criminal cases. Former Assistant U.S. Attorney Patrick Harris for the Eastern District of Arkansas and former Assistant U.S. Attorney Ben Wulff for the Western District of Arkansas provided significant assistance.

    MIL Security OSI

  • MIL-OSI Security: Justice Department Agrees to $215 Million Settlement Agreement Related to Assets of Internet Prostitution Ad Service Backpage.com

    Source: US FBI

    LOS ANGELES – The Justice Department today filed a settlement agreement reached between the parties in the civil forfeiture case involving Backpage.com, a now-shuttered internet forum for prostitution ads that included ads depicting sex work of children, in which $215 million in assets traceable to Backpage’s profits, and previously seized by the government from Backpage and its agents, will be forfeited to the United States.

    The forfeited assets – comprised of cash, cryptocurrency, and one parcel of real estate in San Francisco – will be available for a remission process to compensate victims of the crime. Details about the remission process will be announced at a later date. The forfeiture represents more than 80% of the value of the property seized or restrained in the case.

    “This settlement agreement marks a significant milestone in a criminal case involving the sexual exploitation and trafficking of countless women and children,” said United States Attorney Martin Estrada. “The nine-figure dollar amount forfeited in this case will allow for victims to recover and show that individuals who profit from such exploitation and trafficking risk both prison time and financial ruin.”

    For most of its 14-year existence, Backpage dominated the online market for illegal sex work advertising in the United States. While Backpage offered many categories of advertisements, in most years, more than 90% of Backpage’s revenue and activity occurred in the adult-related ad sections. Backpage monetized these advertisements by allowing a variety of pay-for options such as posting ads across multiple geographic areas and increased ad promotion. The company’s CEO eventually admitted that most of the website’s adult ads were for prostitution.

    In April 2018, several Backpage-related corporate entities, including Backpage LLC, pleaded guilty in Arizona federal court to conspiracy to engage in money laundering. Several Backpage owners and executives also have been convicted in this matter, including Michael Lacey, 76, of Paradise Valley, Arizona, was sentenced to five years in prison; Scott Spear, 74, of Phoenix, was sentenced to 10 years in prison; and John “Jed” Brunst, 72, of Phoenix, was sentenced to 10 years in prison. Lacey is free on bail pending appeal.

    According to court documents and evidence presented at trial, from September 2010 until its seizure by the United States in April 2018, Backpage was the internet’s leading forum for prostitution ads. The conspirators knowingly promoted prostitution via various marketing strategies. For example, they engaged in a reciprocal link program with an independent web forum that permitted “johns” to post reviews of prostitution acts with specific women. Additionally, the conspirators used an automated filter and human moderators to remove terms known to indicate sex-for-money, while still allowing the ads to be posted. Through this attempt to sanitize the ads, the conspirators sought “plausible deniability” for what the conspirators knew to be ads promoting prostitution. Over the life of the conspiracy, the conspirators earned more than $500 million. To preserve the money earned, Lacey, Spear, and Brunst laundered the money through numerous shell companies they created in multiple foreign countries.

    The United States Postal Inspection Service, the FBI, and IRS Criminal Investigation investigated this matter. The United States Attorney’s Office for the District of Arizona, which prosecuted the underlying criminal cases, provided substantial assistance.

    Assistant United States Attorney Jonathan S. Galatzan of the Asset Forfeiture and Recovery Section is prosecuting this case.

    The case name and number in this matter are United States of America v. $1,546,076.35 In Bank Funds Seized from Republic Bank of Arizona Account 1889, et al., CV 18-08420 (C.D. Calif.).

    MIL Security OSI

  • MIL-OSI Security: Pasadena Doctor Agrees to Plead Guilty to Conspiring with Attorney to Bilk More Than $3 Million From California’s Workers’ Compensation Fund

    Source: US FBI

    SANTA ANA, California – A physician who worked for an Inland Empire medical company has agreed to plead guilty to conspiring to defraud California’s workers’ compensation fund of millions of dollars by continuing to work on workers’ compensation matters after being suspended due to a prior health care fraud conviction, the Justice Department announced today. 

    Dr. Kevin Tien Do, 59, of Pasadena, agreed to plead guilty to one count of conspiracy to commit mail fraud and one count of subscribing to a false tax return. He is expected to make his initial appearance this afternoon in United States District Court in Santa Ana.

    In his plea agreement, Do admitted that, from October 2018 to February 2023, he conspired to defraud the state of California of millions of dollars of health care funds by defrauding California’s Subsequent Injuries Benefits Trust Fund (SIBTF). The California SIBTF is a special fund administered by California’s workers’ compensation program to provide additional compensation to injured workers who already had a disability or impairment at the time of a subsequent injury.

    Beginning in 2016, Do began to work for Liberty Medical Group Inc., a Rancho Cucamonga-based medical company, for which he would draft SIBTF-related medical reports that Liberty would then bill to the California SIBTF program. In October 2018, California suspended Do from participating in California’s workers’ compensation program, which included the SIBTF, because he had previously been convicted of federal health care fraud in 2003. Despite his suspension, Do continued to work for Liberty on SIBTF-related workers’ compensation matters.

    Do continued to perform similar actions for Liberty that he had been doing before his October 2018 suspension, including compiling and editing reports related to the SIBTF program. To conceal that Do was unlawfully continuing to participate in the workers’ compensation SIBTF program after his suspension, Liberty’s owner came up with a plan. That plan was that Do would continue to author the SIBTF-related reports, which Liberty would then continue to mail to the California SIBTF for payment. Rather than listing Do’s name on the billing forms and the attached medical reports mailed to the California SIBTF, like they had had done before Do’s suspension, Liberty instead fraudulently listed other doctors’ names on the billing forms and attached medical reports, even though Do had drafted and compiled the reports. Do admitted that Liberty was paid more than $3 million by California SIBTF for such reports that Liberty mailed to the California SIBTF for payment after Do’s October 2018 suspension.

    Do’s plea agreement also details that Liberty’s owner edited Do’s medical reports, even though that co-conspirator was not a doctor or other licensed medical professional. 

    Under California law, shareholders/owners of a medical corporation must be licensed in the practice of medicine or other related medical fields, such as a psychologist, registered nurse, or licensed physician assistant.

    In his plea agreement, Do admitted that real owner of Liberty and Do’s co-conspirator was another person who was not a doctor or other medical professional, but rather, was a California attorney then employed as a prosecutor for the Orange County District Attorney’s Office, and who later became an Orange County Superior Court judge during the conspiracy. That true owner who was Do’s co-conspirator not only was a signatory on Liberty’s bank account, but also issued and signed Liberty’s checks to Do and others. The plea agreement specifies that much of the more than $3 million that the SIBTF paid Liberty during the years following Do’s suspension then flowed to another company controlled by Liberty’s owner and his wife, which totaled to more than $1.5 million.

    Do also admitted that he failed to accurately report to the IRS all the money he had been paid by Liberty. Do admitted that on his 2021 tax return, he failed to report approximately $66,227 of the income that Liberty paid him.

    Once Do enters his guilty plea, he will face a statutory maximum sentence of 20 years in federal prison for the mail fraud count and up to three years in federal prison for the tax fraud count. 

    The FBI, IRS Criminal Investigation, and the California Department of Insurance are investigating this matter.

    Assistant United States Attorneys Charles E. Pell of the Orange County Office and Ryan J. Waters of the Asset Forfeiture and Recovery Section are prosecuting the case.

    MIL Security OSI

  • MIL-OSI Security: Rancho Cucamonga Man Sentenced to More Than Three Years in Prison for Operating ‘Birth Tourism’ Scheme for Affluent Chinese Clients

    Source: US FBI

    LOS ANGELES – A San Bernardino County man was sentenced today to 41 months in federal prison for operating a “birth tourism” scheme that charged Chinese clients tens of thousands of dollars to help them give birth in the United States to obtain birthright U.S. citizenship for their children.

    Michael Wei Yueh Liu (刘维岳), 59, of Rancho Cucamonga, was sentenced by United States District Judge R. Gary Klausner.

    At the conclusion of a four-day trial, a jury on September 13 found Liu and Jing Dong, (董晶), 47, of Rancho Cucamonga, guilty of one count of conspiracy and 10 counts of international money laundering. Dong is expected to be sentenced in the coming weeks.

    From at least January 2012 to March 2015, Liu and Dong ran a maternity house in Rancho Cucamonga. Liu and Dong rented apartments in Southern California to provide short-term housing and provided other services to pregnant women from China who traveled to the United States to give birth so their children would acquire U.S. citizenship. Typically, within one or two months after giving birth, the women returned to China.

    Among the services Liu and Dong provided were assistance on how to obtain visas to enter the United States, customs entry guidance, housing, and transportation in the United States, as well as assistance applying for U.S. legal documents for the children of their customers.

    Liu and Dong advised their customers on how to hide their pregnancies from the immigration authorities. Liu and Dong also knew – or deliberately avoided learning – that their customers lied on their visa applications submitted to immigration authorities to enter the U.S.

    Generally, their customers’ visa applications falsely stated that the purpose of the trip to the United States was for tourism, when it was to give birth, and the length of the stay was days or weeks, when it was in fact months. The visas also misstated the location where the customers intended to stay, which was defendants’ maternity hotel.

    Liu and Dong or their agents also advised their customers to fly to ports of entry with perceived less customs scrutiny, such as Hawaii, before flying to Los Angeles, to wear loose fitting clothing, to favor certain lines at customs that they perceived to be less strict, and on how to answer the customs officials’ questions.

    Liu and Dong received money from overseas and used that money to promote their scheme.

    Homeland Security Investigations, IRS Criminal Investigation, and the FBI investigated this matter. The Irvine Police Department and the San Bernardino County Sheriff’s Department provided substantial assistance.

    Assistant United States Attorneys Gregory W. Staples and Kevin Y. Fu of the Orange County Office prosecuted this case.

    MIL Security OSI

  • MIL-OSI Security: Bitwise Founders Sentenced to 11 Years and Nine Years in Prison for $115 Million Fraud

    Source: US FBI

    FRESNO, Calif. —Jake Soberal, 38, and Irma Olguin, Jr., 44, the founders and leaders of the failed Fresno-based start-up company, Bitwise Industries (“Bitwise”), were sentenced to 11 years and 9 years in prison, respectively, for defrauding people out of approximately $115,000,000, United States Attorney Phillip A. Talbert announced today.

    “Defendants likened themselves to gods and joked about deceiving their well-intentioned investors while committing a massive fraud,” said U.S. Attorney Talbert.  “They lied repeatedly to pull in over $100 million to a dying business venture that they knew never had any meaningful revenue.  To make themselves rich and keep up the façade, they used fabricated bank statements, false financial information, forged documents, and fake loan collateral.  These sentences serve as a reminder of the hazards of such financial crimes, and my office will continue to work with the FBI, IRS Criminal Investigation, and our law enforcement partners to vigorously investigate and prosecute those who commit them.”

    “The willful and egregious fraud carried out by Irma Olguin Jr. and Jake Soberal will have long lasting impacts on not only those who invested in the well-orchestrated scam of Bitwise, but also the nearly 1,000 employees and contractors who abruptly lost their jobs when the Bitwise swindlers ran out of money,” said IRS Criminal Investigation (IRS-CI) Oakland Field Office Assistant Special Agent in Charge Kulbir Mand. “White-collar crimes are damaging to victims, families, and communities alike. IRS-CI and its law enforcement partners are experts at investigating financial crimes and building cases that lead to justice. Today’s sentencing should serve notice that the consequence for committing white-collar crime is severe.”

    “This case demonstrates how disastrous the impact can be when a company’s executives fail to conduct themselves ethically and lawfully.  Bitwise Industries co-CEOs Jake Soberal and Irma Olguin, Jr. repeatedly lied to investors and lenders to keep their massive Ponzi scheme afloat, despite knowing that the business model would never generate positive revenue. The $115 million loss is significant, but the damage to the professional reputations of innocent parties and the loss of more than 900 jobs and associated benefits employees depended on will have a lasting, negative impact on the economy and individual lives,” said FBI Sacramento Special Agent in Charge Sid Patel. “The FBI remains steadfast, safeguarding our economy by working with all partner agencies to ensure that those who exploit positions of trust to commit large-scale corporate frauds are held accountable for their criminal activity.”

    According to court records, Bitwise was, and still is, the biggest startup company to come from California’s Central Valley.  The company’s objective was to use technology to create jobs for underserved groups of people, revitalize blighted urban areas, and show that such a project could be profitable.

    Olguin, Jr. and Soberal received national media attention by appearing in publications like Forbes Magazine and giving Ted Talks where they portrayed Bitwise as being a success.  They also made a substantial annual salary.  By early 2022, however, the company was not generating any revenue and was running low on funds.  Thereafter, Olguin, Jr. and Soberal fabricated financial information for its board and for investor materials and doctored audit reports to make it appear as though Bitwise was generating revenues and turning a profit.  They also altered bank statements and forged bank representatives’ signatures on bank correspondence to inflate the company’s cash balances.  They did so to convince people that Bitwise was excelling when the company was actually failing.

    The following are illustrative examples of Olguin, Jr. and Soberal’s fraud:

    • In a February 2022 presentation and July 2022 prospectus that were circulated to investors, Olguin, Jr. and Soberal represented that Bitwise’s cash balance was over $44,000,000 as of the end of 2021.  They also represented that the company’s revenue was more than $58,000,000.  In reality, the company’s cash balance was less than $12,000,000 at that time and its revenue was non-existent. 
    • In June and July 2022, Olguin, Jr. and Soberal falsely represented to a California-based investment firm that Bitwise had secured a $150,000,000 investment from another, London-based investment firm.  This was done to convince the California-based investment firm to purchase several buildings that Bitwise owned.  Several months later, Soberal falsely represented to another lender that Bitwise still owned those buildings to to provide collateral for another loan from another lender of millions of dollars. 
    • In a March 2023 presentation circulated to investors, Olguin, Jr. and Soberal represented that Bitwise’s cash balance was over $77,000,000 as of the end of 2022.  They also represented that the company’s revenue was more than $143,000,000.  In reality, the company’s cash balance was less than $5,000,000 at that time and its revenue nominal. 
    • Also in March 2023, Olguin, Jr. and Soberal provided an investor with an altered version of an audit of Bitwise that was previously conducted by an international audit firm.  They altered the audit to make it appear as though Bitwise’s revenue was 300 percent higher than the true number. 
    • Also in March 2023, Soberal represented to a long-time Bitwise employee that the company had sufficient resources on-hand to induce the employee to make a significant loan to the company.

    This pattern continued until the end of May 2023 when Bitwise ran out of money and the company collapsed.

    Olguin, Jr. was a computer engineer who had previously run another technology company, and Soberal was an attorney who had previously practiced at a law firm doing intellectual property work.  Moreover, the defendants hired unqualified family members and friends, which allowed them to compartmentalize information and work in secret to spin the false statements needed to conceal and continue with their fraud.  For these reasons, Olguin, Jr. and Soberal received special sentencing enhancements. 

    This case is the product of an investigation by the FBI and IRS Criminal Investigation.  Assistant United States Attorneys Joseph Barton and Henry Z. Carbajal III prosecuted the case.

    MIL Security OSI

  • MIL-OSI Security: Las Vegas Resident Sentenced to Prison for COVID-19 Fraud Scheme

    Source: US FBI

    LAS VEGAS – A Las Vegas woman was sentenced Wednesday by United States District Judge James C. Mahan to 30 months in prison to be followed by three years of supervised release for fraudulently seeking over $1 million in COVID-19 Paycheck Protection Program (PPP) loans.

    According to court documents, from April 2020 to July 2020, Karen Chapon, aka Karen Hannafious, made multiple false statements about her companies’ respective business operations and payroll expenses, and submitted false documents to support six fraudulent PPP loan applications, including false federal tax filings. As part of the fraudulent loan applications, Chapon falsely stated that she had not been convicted of a felony in the past five years, but in fact, she pleaded guilty to felony fraud offenses in 2016. She received four loans totaling approximately $596,931. Chapon used fraudulently obtained funds for her own benefit, including the purchase of a Mercedes Benz SUV.

    In August 2023, Chapon pleaded guilty to one count of bank fraud. In addition to the prison term, Chapon was ordered to pay $589,484.13 in restitution.

    The Coronavirus Aid, Relief, and Economic Security (CARES) Act is a federal law enacted March 29, 2020. It is designed to provide emergency financial assistance to millions of Americans who are suffering the economic effects resulting from the COVID-19 pandemic. One source of relief provided by the CARES Act is the authorization of up to $349 billion in forgivable loans to small businesses for job retention and certain other expenses through the PPP. In April 2020, Congress authorized over $300 billion in additional PPP funding.

    The PPP allows qualifying small businesses and other organizations to receive loans with a maturity of two years and an interest rate of one percent. Businesses must use PPP loan proceeds for payroll costs, interest on mortgages, rent and utilities. The PPP allows the interest and principal to be forgiven if businesses spend the proceeds on these expenses within a set time period and use at least a certain percentage of the loan towards payroll expenses.

    United States Attorney Jason M. Frierson for the District of Nevada; Principal Deputy Assistant Attorney General Nicole M. Argentieri, head of the Justice Department’s Criminal Division; Special Agent in Charge Spencer L. Evans for the FBI; Acting Inspector General Heather M. Hill for the Treasury Inspector General for Tax Administration (TIGTA); and Special Agent in Charge Weston King for the U.S. Small Business Administration Office of Inspector General (SBA-OIG), Western Region made the announcement.

    This case was investigated by the FBI, TIGTA, and SBA OIG. Assistant United States Attorney Jessica Oliva and Trial Attorneys Lucy Jennings and Jennifer Bilinkas of the Criminal Division’s Fraud Section prosecuted the case.

    In May 2021, the Attorney General established the COVID-19 Fraud Enforcement Task Force to marshal the resources of the Department of Justice in partnership with agencies across government to enhance efforts to combat and prevent pandemic-related fraud. The Task Force bolsters efforts to investigate and prosecute the most culpable domestic and international criminal actors and assists agencies tasked with administering relief programs to prevent fraud by augmenting and incorporating existing coordination mechanisms, identifying resources and techniques to uncover fraudulent actors and their schemes, and sharing and harnessing information and insights gained from prior enforcement efforts. For more information on the department’s response to the pandemic, please visit www.justice.gov/coronavirus.

    Anyone with information about allegations of attempted fraud involving COVID-19 can report it by calling the Department of Justice’s National Center for Disaster Fraud Hotline at 866-720-5721 or via the NCDF Web Complaint Form at: https://www.justice.gov/disaster-fraud/ncdf-disaster-complaint-form.

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    MIL Security OSI

  • MIL-OSI Security: North Carolina Man Pleads Guilty to His Role in Scheme That Defrauded Hundreds of Companies Out of Millions of Dollars

    Source: US FBI

    ROCHESTER, N.Y. – U.S. Attorney Trini E. Ross announced today that Nicholas Scarantino, 30, of North Carolina, pleaded guilty before Chief U.S. District Judge Elizabeth A. Wolford to conspiracy to commit mail fraud, which carries a maximum penalty of 20 years in prison and a $250,000 fine.

    Assistant U.S. Attorney Richard A. Resnick, who is handling the case, stated that Scarantino owned Direct Chemicals in the State of California. Between July and November 2021, he and others mailed thousands of fictitious invoices in the name of Direct Chemicals to victim companies located all over the United States. Approximately 873 victim companies were tricked and defrauded into paying these fictitious invoices, totaling of approximately $861,268.66. Several companies in the Western District of New York were victimized. The companies are located in Henrietta, Lakewood, Brockport, Andover, Rochester, Avon, Tonawanda, and Niagara Falls.

    The plea is the result of an investigation by the Federal Bureau of Investigation, under the direction of Special Agent-in-Charge Matthew Miraglia, the U.S. Postal Inspection Service, under the direction of Inspector in Charge Ketty Larco-Ward, Boston Division, and the Internal Revenue Service-Criminal Investigations, under the direction of Special Agent-in-Charge Thomas M. Fattorusso.

    Sentencing is scheduled for March 17, 2025, before Judge Wolford.

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    MIL Security OSI

  • MIL-OSI Security: Leader of International Drug Trafficking Organization Operating in Lane County Sentenced to Federal Prison

    Source: US FBI

    PORTLAND, Ore.—The leader of an international drug trafficking organization operating in Lane County, Oregon, responsible for trafficking large quantities of methamphetamine, heroin, and cocaine into the state between 2018 and 2020, was sentenced to federal prison today.

    Victor Diaz-Ramirez, 33, was sentenced to 135 months in federal prison and five years’ supervised release.

    “While communities across our state continue to struggle with the ongoing drug crisis, there are criminal enterprises, like the Diaz-Ramirez drug trafficking organization, whose sole purpose is to profit from addiction and suffering. This far-reaching investigation demonstrates the deep commitment of all involved law enforcement agencies to combatting drug trafficking and keeping our communities safe,” said Nathan J. Lichvarcik, Chief of the U.S. Attorney’s Office Eugene and Medford Branch Offices.

    “Drug traffickers like Mr. Diaz-Ramirez prey on our communities by peddling large amounts of methamphetamine, heroin, and cocaine, often to our most vulnerable,” said David F. Reames, Special Agent in Charge, U.S. Drug Enforcement Administration (DEA) Seattle Field Division. “I am gratified that the hard work of DEA, the U.S. Attorney’s Office and our many partners from law enforcement agencies across Oregon led to the lengthy sentence Mr. Diaz-Ramirez received in this case. Justice was truly served.”

    According to court documents, from at least March 2018 through August 2020, while operating out of Mexico, Diaz-Ramirez helped lead an international drug trafficking organization responsible for trafficking large quantities of methamphetamine, heroin, and cocaine from Mexico into the United States. Diaz-Ramirez’s organization used a network of associates to transport the drugs from Southern California to Oregon and deliver them to local distributors in exchange for cash. 

    As part of this investigation, law enforcement seized more than 178 pounds of methamphetamine, 12 pounds of heroin, six pounds of fentanyl, 18 rifles, three rifle optics, and ammunition. Investigators also forfeited approximately $1.2 million from the organization, including more than $400,000 in cash. In total, 35 people—including sources of supply in Mexico, couriers, local cell operators in Lane County, and first and second level distributors responsible for sales in and around Eugene—were charged and have been convicted for their roles in Diaz-Ramirez’s organization.

    On August 5, 2020, a federal grand jury in Eugene returned an indictment charging Diaz-Ramirez with conspiracy to distribute methamphetamine. On November 1, 2023, Diaz-Ramirez pleaded guilty to a one-count superseding criminal information charging him with conspiracy to possess with intent to distribute methamphetamine.

    This case was investigated by DEA, FBI, IRS-Criminal Investigation, U.S. Marshals Service, Springfield Police Department, Eugene Police Department, Lane County Sherriff’s Office, Oregon State Police, Linn Interagency Narcotics Enforcement Team (LINE), and Douglas Interagency Narcotics Enforcement Team (DINT). It was prosecuted by Joseph Huynh and Judi Harper, Assistant U.S. Attorneys for the District of Oregon.

    This prosecution is the result of an Organized Crime Drug Enforcement Task Force (OCDETF) investigation. OCDETF identifies, disrupts, and dismantles the highest-level drug traffickers, money launderers, gangs, and transnational criminal organizations that threaten the U.S. by using a prosecutor-led, intelligence-driven, multi-agency approach that leverages the strengths of federal, state, and local law enforcement agencies against criminal networks.

    MIL Security OSI

  • MIL-OSI Security: Member of International Drug Trafficking Organization Operating in Lane County Sentenced to Federal Prison

    Source: US FBI

    EUGENE, Ore.—A Springfield, Oregon member of an international drug trafficking organization operating in Lane County, Oregon, was sentenced to federal prison.

    Rodolfo Arroyo-Segoviano, 38, was sentenced to 145 months in federal prison and three years’ supervised release. 

    According to court documents, between April and August 2020, Arroyo-Segoviano managed the local distribution network for an international drug trafficking organization responsible for trafficking large quantities of methamphetamine, heroin, and cocaine from Mexico into the United States. While the organization leaders operated out of Mexico, Arroyo-Segoviano was responsible for the local operations in Oregon. He coordinated the receipt, storage, and distribution of methamphetamine, the collection of drug proceeds, and payment to organization leadership in Mexico. Arroyo-Segoviano also supervised the local associates, including recruitment, pay, and directing activities.    

    As part of this investigation, law enforcement seized more than 178 pounds of methamphetamine, 12 pounds of heroin, six pounds of fentanyl, 18 rifles, three rifle optics, and ammunition. Investigators also forfeited approximately $1.2 million from the organization, including more than $400,000 in cash. In total, 35 people—including sources of supply in Mexico, couriers, local cell operators in Lane County, and first and second level distributors responsible for sales in and around Eugene—were charged and have been convicted for their roles in the drug trafficking organization.

    “The prosecution of this international drug trafficking organization represents the tireless dedication of our federal, state, and local law enforcement partners combatting the drug trafficking plaguing our communities,” said Natalie Wight, U.S. Attorney for the District of Oregon.

    On July 31, 2020, Arroyo-Segoviano was charged by criminal complaint with conspiracy to distribute methamphetamine. On April 3, 2024, Arroyo-Segoviano pleaded guilty to a one-count superseding criminal information charging him with conspiracy to possess with intent to distribute methamphetamine.

    This case was investigated by DEA, FBI, IRS-Criminal Investigation, U.S. Marshals Service, Springfield Police Department, Eugene Police Department, Lane County Sherriff’s Office, Oregon State Police, Linn Interagency Narcotics Enforcement Team (LINE), and Douglas Interagency Narcotics Enforcement Team (DINT). It was prosecuted by Joseph Huynh and Judi Harper, Assistant U.S. Attorneys for the District of Oregon.

    This case is part of an Organized Crime Drug Enforcement Task Forces (OCDETF) investigation. OCDETF identifies, disrupts, and dismantles the highest-level drug traffickers, money launderers, gangs, and transnational criminal organizations that threaten the United States by using a prosecutor-led, intelligence-driven, multi-agency approach that leverages the strengths of federal, state, and local law enforcement agencies against criminal networks.

    MIL Security OSI

  • MIL-OSI Security: Anchorage Man Sentenced to 14 Years for Trafficking Fentanyl, Methamphetamine

    Source: US FBI

    FAIRBANKS, Alaska – An Anchorage man was sentenced on Sept. 5 to 14 years in prison for distributing large quantities of controlled substances in Alaska.

    According to court documents, Darrell Latory Moss Sr, 45, sold over 1,300 grams of methamphetamine and 100 fake prescription pills containing fentanyl over a three-month period.

    The defendant was arrested in Bethel, Alaska, in March 2023. The defendant pleaded guilty to one count of distributing a controlled substance, in violation of 21 U.S.C.§841(a)(1). Moss was sentenced to 14 years in federal prison, five years’ supervised release, and he is required to forfeit two vehicles and over $8,500 cash.

    ”Mr. Moss’s sentence brings us another step closer in our efforts to keep Alaskans safe,” said U.S. Attorney S. Lane Tucker for the District of Alaska. “Drugs are affecting communities and villages large and small and create serious public safety issues. Our office will continue to prioritize working with our Federal, State and local law enforcement partners to identify, investigate and prosecute people who choose to push dangerous drugs out into our communities. Their actions will not be tolerated.”

    “This defendant sought to financially gain from distributing dangerous drugs, including deadly fentanyl pills disguised as oxycodone pills, at the expense of Alaska’s communities,” said Special Agent in Charge Antony Jung of the FBI Anchorage Field Office. “This sentencing ensures accountability for those crimes, and is a result of law enforcement partnerships across Alaska working to keep illicit drugs out of our communities.”

    The FBI Anchorage Field Office, with assistance from the Drug Enforcement Administration (DEA), Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF), IRS Criminal Investigation, Alaska State Troopers, Anchorage High Intensity Drug Trafficking Area (HIDTA) team, Fairbanks HIDTA team and Mat-Su Valley HIDTA team investigated the case.

    Assistant U.S. Attorney Carly Vosacek and former Assistant U.S. Attorney Ryan Tansey prosecuted the case.

    This investigation and prosecution were part of the Organized Crime Drug Enforcement Task Force (“OCDETF”), which identifies, disrupts, and dismantles the highest-level drug traffickers, money launderers, gangs, and transnational criminal organizations that threaten the United States by using a prosecutor-led, intelligence-driven, multi-agency approach that leverages the strengths of federal, state, and local law enforcement agencies against criminal networks.

    ###

    MIL Security OSI

  • MIL-OSI Security: FBI Warning: ‘Tis the Season for Holiday Scams

    Source: US FBI

    As we enter the 2022 holiday season, Arkansas residents must remain mindful of criminals who care less about giving and more about stealing. Shoppers looking for a good deal this holiday season need to be aware of aggressive and deceptive scams designed by criminals to steal money and personal information. According to the FBI’s Internet Crime Complaint Center (IC3), Americans lost over $6.9 billion to fraudsters just in 2021, including more than $335 million in online shopping and non-delivery scams. This year, FBI Little Rock wants Arkansas shoppers to enjoy a scam-free holiday season by remaining vigilant against the below schemes.

    Online Shopping Scams: Criminals often offer too-good-to-be-true deals via phishing emails, text messages, and fake advertisements on social media. Perhaps you were looking to buy tickets to an upcoming concert and found just what you were looking for—at a good deal—in an online marketplace? Those tickets could end up being bogus. Or maybe you just located a new, hard-to-find gaming system… but in reality, you clicked on a link which gave a criminal the ability to download malware onto your device. Bottom line is if a deal looks too good to be true, it probably is! Stay clear of unfamiliar sites offering unrealistic discounts on brand-name merchandise. Scammers frequently prey on bargain hunters by advertising “One-Day Only” promotions for recognizable brands. Without employing a skeptical eye, consumers may end up paying for an item, giving away personal information, and receiving nothing in return except a compromised identity.

    Fraudulent Social Media Posts: Consumers should beware of posts on social media sites that appear to offer special deals, vouchers, or gift cards. Some may appear as holiday promotions or contests. Others may be sent by friends who shared a link on popular social media sites. These scams frequently lead consumers to participate in online surveys designed to steal personal information. Before you click on a social media advertisement, do your due diligence and check the legitimacy of the website before providing any personal or credit card information.

    Charity Scams: Charity-related frauds increase during the holidays as individuals look to donate money to those less fortunate. Criminals use phone calls, email campaigns, and fake websites to solicit on behalf of fraudulent charities. Scammers target people who want to donate to charity, then hoard well-intentioned donations while those most in need never see a dime.

    Steps to avoid holiday fraud schemes:

    • Before shopping online, secure all your financial accounts with strong passphrases. Additionally, use different passphrases for each financial account.
    • Routinely check bank and credit card statements, especially after making online purchases and in the weeks following the holiday season.
    • Never give personal information— such as your date of birth, Social Security number, or home address— to anyone you do not know.
    • Be highly suspicious of promotions and giveaways which require your personal information.
    • Prior to donating to any charity, verify they have a valid Taxpayer Identification Number by visiting their website or calling the charity directly.

    Reporting fraud: Shoppers who suspect they’ve been victimized should immediately contact their financial institution, then call their local law enforcement agency or FBI Little Rock at (501) 221-9100. Victims of holiday scams are also encouraged to file a complaint with the FBI at www.ic3.gov.

    MIL Security OSI

  • MIL-OSI Security: Fort Smith Arms Dealer Arrested in Austin, Texas

    Source: US FBI

    FORT SMITH – A Fort Smith man was arrested yesterday in Austin, Texas on criminal charges related to his alleged possession of an unregistered destructive device; namely, an improvised explosive bomb, which was not registered to him in the National Firearms Registration and Transfer Record as required by law. Mehta’s arrest ended a six-day manhunt, in which the public’s assistance was solicited in locating the defendant, who was assumed to be armed and dangerous.

    According to court documents, Neil Ravi Mehta, 31, was found to be in possession of an “improvised explosive bomb” during a federal search warrant executed at his residence on Free Ferry Road, in Fort Smith, Ark.  Law enforcement officers located the device in the top left corner of the kitchen island.  The device was x-rayed by bomb technicians on-scene, made safe, and the evidence was collected.  The following images were taken during the execution of the search warrant: 

    Mehta is charged in a Criminal Complaint with a single count of Unlawful Possession of an Unregistered Destructive Device. A Grand Jury will later hear evidence related to this investigation and determine whether additional criminal charges will be filed against Mehta.  If convicted of the charge of Unlawful Possession of an Unregistered Destructive Device, Mehta faces a maximum penalty of ten years in prison. A federal district court judge will determine any sentence after considering the U.S. Sentencing Guidelines and other statutory factors.

    U.S. Attorney David Clay Fowlkes of the Western District of Arkansas made the announcement.

    This is a joint investigation involving the following federal law enforcement agencies:  the Federal Bureau of Investigation (FBI); the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF); the U.S. Department of Commerce (DOC), Bureau of Industry and Security (BIS), Office of Export Enforcement (OEE); the Internal Revenue Service-Criminal Investigation (IRS-CI); and the U.S. Department of Labor, Office of the Inspector General (DOL-OIG).

    Assistant U.S. Attorney Steven Mohlhenrich and First Assistant U.S. Attorney Kenneth Elser are prosecuting the case.

    A criminal complaint is merely an allegation, and all defendants are presumed innocent until proven guilty beyond a reasonable doubt in a court of law.

    MIL Security OSI

  • MIL-OSI Security: Former Commerce City Manager and Former Baldwin Park City Attorney Bribery Guilty Pleas and Plea Agreements Unsealed

    Source: US FBI

    LOS ANGELES – Two former top city officials in Commerce and Baldwin Park have pleaded guilty to participating in a scheme involving bribes in exchange for a corrupt Baldwin Park politician’s votes and influence over his city’s cannabis permitting process, the Justice Department announced today. 

    Edgar Pascual Cisneros, 42, of Montebello, who served as Commerce’s city manager from November 2017 to December 2023, pleaded guilty on November 6, 2023, to federal bribery. Robert Manuel Nacionales Tafoya, 62, of Redondo Beach, who served as Baldwin Park’s city attorney from December 2013 to October 2022, pleaded guilty on December 5, 2023, to federal bribery and tax evasion charges.

    Federal prosecutors today unsealed the criminal charges and plea agreements, in which both Cisneros and Tafoya agreed to cooperate in ongoing public corruption investigations. 

    According to the plea agreements, shortly after Baldwin Park began issuing marijuana permits in June 2017, then-Baldwin Park City Councilmember Ricardo Pacheco solicited bribes from companies seeking those permits. Cisneros helped a company obtain a marijuana permit and related approvals through approximately $45,000 in bribes and that the company promised to pay Cisneros at least $235,000 to help secure the permit. Tafoya facilitated a bribery scheme involving former Compton City Councilmember Isaac Galvan, in which Galvan sought to obtain a marijuana permit for his consulting client also through bribes to Pacheco. Tafoya further admitted to evading payment of approximately $650,000 in federal tax liability.

    Pacheco pleaded guilty in June 2020 to a federal bribery charge unrelated to the marijuana-permit scheme. Pacheco further admitted to orchestrating bribery schemes involving Tafoya and Gabriel Chavez, a former San Bernardino County planning commissioner who pleaded guilty to a federal bribery charge in November 2022. Pacheco’s sentencing hearing is scheduled for February 2025. Chavez’s sentencing hearing is scheduled for April 2025.

    In September 2023, Galvan and his consulting client, Yichang Bai, were arrested on a federal grand jury indictment alleging they paid $70,000 in bribes to Pacheco in exchange for his vote and support for marijuana permits for Bai’s company, W&F International Corp. Both men have pleaded not guilty. Their trial is scheduled for June 10, 2025.

    An indictment contains allegations that a defendant has committed a crime. Every defendant is presumed innocent until and unless proven guilty beyond a reasonable doubt.

    The FBI and IRS Criminal Investigation are investigating these matters.     

    Assistant United States Attorneys Thomas F. Rybarczyk, Michael J. Morse, and Lindsey Greer Dotson of the Public Corruption and Civil Rights Section are prosecuting these cases.

    Any member of the public who has information related to this or any other public corruption matter is encouraged to send information to the FBI’s tip line at tips.fbi.gov or to contact the FBI’s Los Angeles Field Office at (310) 477-6565.

    MIL Security OSI

  • MIL-OSI Security: Yuba County Man Sentenced to Two Years and 11 Months in Prison for Submitting False Claims Against the United States in Relation to a COVID-19 Fraud Scheme

    Source: US FBI

    SACRAMENTO, Calif. — Jason Toland, 44, of Wheatland, was sentenced today by U.S. District Judge Dale A. Drozd to two years and 11 months in prison for submitting false claims against the United States related to COVID-19 pandemic tax credits, U.S. Attorney Phillip A. Talbert announced. Toland was also ordered to pay $2,078,462 in restitution to the Internal Revenue Service (IRS) and the Small Business Administration (SBA).

    According to court documents, Toland attempted to obtain more than $13.4 million in COVID‑19 pandemic relief funds by filing multiple false tax returns with the IRS seeking refunds for the Employee Retention Credit and the COVID Sick and Family Leave Credit. Toland used shell companies that had no real employees and no actual business activity to seek more than $11 million in such tax refunds to which he was not entitled. In addition, between 2020 and 2023, Toland used the shell companies to fraudulently obtain a total of more than $1.7 million in Paycheck Protection Program (PPP) and Economic Injury Disaster Loan (EIDL) funds. All these tax credits and programs were intended to alleviate the economic harm caused by the COVID-19 pandemic on real businesses with real employees and operating expenses.

    Of the more than $13.4 million that he sought through false tax returns and fraudulent loan applications, Toland successfully obtained more than $1.95 million. All the funds Toland received went to his own personal enrichment.

    “The COVID-19 Fraud Strike Force continues to pursue pandemic fraud, including the abuse of tax credits for personal gain,” said U.S. Attorney Talbert. “Today’s sentence demonstrates that false claims targeting credits meant for real businesses suffering real consequences of the pandemic will be identified and prosecuted.”

    “Mr. Toland’s fraudulent and nefarious scheming took aim at funds designated to help both small businesses and American citizens in the midst of a global pandemic,” said IRS Criminal Investigation Oakland Field Office Acting Special Agent in Charge Michael Mosley. “IRS-CI does not and will not sit idly by while financial criminals seek to exploit the American tax system. We are the experts in financial investigations, and we build cases that result in justice.”

    This case was the product of an investigation by IRS-Criminal Investigation in collaboration with the IRS’s Nationally Coordinated Investigation Unit with assistance from the SBA Office of Inspector General and the Federal Bureau of Investigation. Assistant U.S. Attorney Denise N. Yasinow prosecuted the case.

    This effort is part of a California COVID-19 Fraud Enforcement Strike Force operation, one of five interagency COVID-19 fraud strike force teams established by the U.S. Department of Justice. The California Strike Force combines law enforcement and prosecutorial resources in the Eastern and Central Districts of California and focuses on large-scale, multistate pandemic relief fraud perpetrated by criminal organizations and transnational actors. The strike forces use prosecutor-led and data analyst-driven teams to identify and bring to justice those who stole pandemic relief funds. 

    MIL Security OSI

  • MIL-OSI Security: Florida Man Pleads Guilty to Multimillion-Dollar Investment Fraud Schemes and Conspiracy to Launder Money

    Source: US FBI

    Defendant Also Admitted to Disaster Relief Loan Fraud and Destroying Evidence to Obstruct Government Investigation

    SAN FRANCISCO – Thomas Aaron Signorelli pleaded guilty today in federal court to one count of bank fraud, two counts of wire fraud, one count of conspiracy to commit wire fraud, one count of theft of government property, one count of destruction of records, and one count of conspiracy to launder money.

    Signorelli, 46, of West Palm Beach, Fla., was charged by information on Sept. 19, 2024.  In pleading guilty to all seven counts in the information, Signorelli admitted that beginning in January 2021 to around December 2023, he falsely claimed he could assist individuals and entities in need of capital by raising funds, obtaining loans, and securing profitable investments through his company WS Capital, which was registered with the Securities and Exchange Commission.  In fact, Signorelli did not raise capital, obtain loans, or secure profitable investments, and instead used the victims’ funds to pay his personal and living expenses, as well as to pay back other victims.

    Signorelli engaged in one of the fraud schemes with his co-conspirator David Scott Cacchione.  As part of that scheme, Signorelli and Cacchione convinced investors that their money would be used to purchase accounts receivables that did not exist.  Rather than use the funds as promised, Signorelli typically shared a portion of the funds with Cacchione and used the remainder to pay personal and living expenses and repay other victims.  Through his various schemes, Signorelli defrauded individuals and entities of more than $2,500,000.  Signorelli further admitted that he conspired with an attorney in Florida to launder fraud proceeds through the attorney’s client trust account in order to disguise the source and nature of the fraud proceeds.

    The plea agreement also describes that, in December 2021, Signorelli was introduced to an individual who claimed to be looking for someone to launder large sums of drug trafficking proceeds.  Signorelli offered to use WS Capital accounts to launder the supposed drug trafficking proceeds and accepted approximately $150,000 in government funds from an undercover government agent.  Instead of laundering those funds, Signorelli stole the money and used it to pay his personal expenses.

    Signorelli further admitted that he caused applications for a Paycheck Protection Program loan and an Economic Injury Disaster Loan to be submitted to the Small Business Administration (SBA) on behalf of a Napa real estate venture that he had formed.  Signorelli made false representations about the venture’s revenues, payroll, and employee count in order to obtain over $50,000 in disaster relief loans.

    Finally, in August 2022, Signorelli learned that the FBI had obtained a warrant to search his mobile phone.  As detailed in his plea agreement, prior to turning in his mobile phone, Signorelli deleted electronic communications on his device in order to obstruct the government’s investigation.

    The announcement was made by United States Attorney Ismail J. Ramsey, FBI Special Agent in Charge Robert Tripp, IRS-CI Oakland Field Office Acting Special Agent in Charge Michael Mosley, and Small Business Administration (SBA) Office of Inspector General (OIG) Special Agent in Charge of the Western Region Weston King.

    Signorelli remains free on a $200,000 appearance bond imposed on Sept. 20, 2024.  His sentencing hearing is scheduled for Mar. 24, 2025 before the Honorable James Donato, U.S. District Court Judge.  The maximum statutory penalty for each count is set forth below.

    OFFENSE

    STATUTE

    MAXIMUM PENALTY

    Bank Fraud 18 U.S.C. § 1344 30 years’ imprisonment; $1,000,000 fine; 5 years’ supervised release; $100 special assessment; forfeiture and restitution
    Wire Fraud 18 U.S.C. § 1343 20 years’ imprisonment; $250,000 or twice the gross gain or loss, whichever is greater; 3 years’ supervised release; $100 special assessment; forfeiture and restitution
    Conspiracy to Commit Wire Fraud 18 U.S.C. § 1349 20 years’ imprisonment; $250,000 or twice the gross gain or loss, whichever is greater; 3 years’ supervised release; $100 special assessment; forfeiture and restitution
    Theft of Government Property 18 U.S.C. § 641 10 years’ imprisonment; $250,000; 3 years’ supervised release; $100 special assessment; forfeiture and restitution
    Destruction, Alteration, and Falsification of Records in  Federal Investigations 18 U.S.C. § 1519 20 years’ imprisonment; $250,000; 3 years’ supervised release; $100 special assessment; forfeiture and restitution
    Conspiracy to Launder Money 18 U.S.C. § 1956(h) 20 years’ imprisonment; $500,000 or twice the value of the property involved in the transaction, whichever is greater; 3 years’ supervised release; $100 special assessment; forfeiture and restitution

    However, any sentence will be imposed by the court only after consideration of the U.S. Sentencing Guidelines and the federal statute governing the imposition of a sentence, 18 U.S.C. § 3553.

    Signorelli’s co-conspirator Cacchione pleaded guilty on Aug. 14, 2024, and was sentenced by Judge Donato on Nov. 4, 2024, to a 40-month term of imprisonment.

    Assistant U.S. Attorney Garth Hire is prosecuting the case.  The prosecution is the result of an investigation by the FBI, IRS-CI, and SBA OIG.
     

    MIL Security OSI

  • MIL-OSI Security: The Diamond Desk Corp. and PetersenLowe, LLC Operators Sentenced in Multimillion-Dollar Diamond Investment Fraud Scheme

    Source: US FBI

    Note: This press release was corrected to reflect the proper announcing officials.  

    MIAMI – On May 15, 2025, Adam Jonathan Lowe, 43, of West Pittston, Pennsylvania, was sentenced to over 6 years in federal prison by the Honorable David Leibowitz, stemming from his conviction for conspiracy to commit wire fraud in violation of Title 18, United States Code, Section 1349, wire fraud in violation of Title 18, United States Code, Section 1343, mail fraud in violation of Title 18, United States Code, Section 1341, and engaging in monetary transactions in criminally derived proceeds, in violation of Title 18, United States Code, Section 1957.  Upon release from custody, Lowe must serve three years of supervised release and pay restitution to the victims of his offense.

    Previously, on May 13, 2025, co-defendant Murray Todd Petersen, 73, of Fair Oaks, California, was sentenced to 9 years in federal prison by the Honorable James I. Cohn stemming from his conviction after a seven-day jury trial in Fort Lauderdale, Florida for conspiracy to commit wire fraud in violation of Title 18, United States Code, Section 1349 and wire fraud in violation of Title 18, United States Code, Section 1343.  Upon release from custody, Petersen must serve three years of supervised release and pay restitution to the victims of his offense.

    On October 18, 2024, co-defendant Scott Schafer, 62, of Pembroke Pines, Florida, was sentenced to five years probation stemming from his stemming from his conviction for conspiracy to commit wire fraud in violation of Title 18, United States Code, Section 1349.

             As outlined in court documents and trial testimony, Adam Jonathan Lowe, as the president of The Diamond Desk and as the manager of PetersenLowe, LLC., was the supplier of fancy-colored diamonds sourced worldwide.  Murray Todd Petersen worked as a salesman for PetersenLowe, LLC., who induced investors to purchase Lowe’s fancy-colored diamonds using materially false and fraudulent representations concerning the safety and security of the investments, the value of the investments, the expected profits and rates of return, and the use of investors’ funds. After selling his victims expensive fancy-colored diamonds supplied with fraudulent overvalued appraisals from co-defendant Scott Schafer, Petersen instructed his clients to hold onto their investments often for one to two years prior to looking to liquidate. When trying to cover his investors cash out demands at the overpriced appraisal prices, Petersen and Lowe used another false representation of a China investment program, where they would purportedly invest the victims’ money into the Chinese diamond market with a purported guaranteed five to eight percent monthly dividend return on investment. Unbeknownst to their victims, this new investment program was really a Ponzi scheme in disguise designed to pay off his first round of investor clients. When customers began to complain about missing promised returns and highly inaccurate overvalued appraisals, the scheme pivoted again to a theft model, where investors prepaid for diamonds that were never delivered by either Lowe or Petersen. Petersen took approximately $850,000 in sales commissions from his victims, which he used to pay off his high IRS tax liens and cover his business operating expenses.  In total, the scheme netted approximately $13 million and defrauded in excess of 100 victims.

             U.S. Attorney Hayden P. O’Byrne for the Southern District of Florida and acting Special Agent in Charge Brett D. Skiles of FBI Miami made the announcement.   

             FBI Miami investigated the case. Assistant U.S. Attorneys Marc Anton and Latoya Brown prosecuted the case.  Assistant U.S. Attorney Marx Calderon is handling asset forfeiture.

             You may find a copy of this press release (and any updates) on the website of the United States Attorney’s Office for the Southern District of Florida at www.justice.gov/usao-sdfl.

             Related court documents and information may be found on the website of the District Court for the Southern District of Florida at www.flsd.uscourts.gov  or at http://pacer.flsd.uscourts.gov, under case number 23-cr-60225.

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    MIL Security OSI

  • MIL-OSI Security: Nevada CPA Sentenced to Three Years in Prison in False Tax Return Scheme

    Source: US FBI

    LAS VEGAS – A Nevada man was sentenced Tuesday to three years in prison for willfully aiding and assisting the filing of false tax returns, in connection with a scheme to sell purported investment opportunities to clients that he falsely claimed would entitle them to IRS tax deductions.

    According to court documents and statements made in court, Lance K. Bradford, of Henderson, was a certified public accountant and founder and manager of LL Bradford & Company (LLB). LLB performed accounting-related work, including tax preparation, audit and consulting services. Bradford also operated a real estate business that developed office buildings and other real property. In connection with Bradford’s real estate development activities, he operated and controlled a real estate investment partnership entity.

    In 2011, Bradford began offering LLB’s high-net-worth clients an “investment opportunity” through which the clients would make a payment to his partnership entity and, in exchange, receive a large tax deduction of approximately five to seven times the amount of money the client “invested.” Bradford advised that the clients’ payments would entitle them to claim the large tax deduction based on losses derived from the partnership entity, even though he knew the tax laws did not permit the sale of such deductions in exchange for an investment of money, and the partnership did not incur the losses or depreciation in the amounts represented by Bradford. Bradford also did not report the purported investments as losses on the clients’ tax returns as promised. Instead, he caused the clients’ returns to report large false deductions for cost of goods sold, professional and consulting fees or nonpassive losses. In total, Bradford’s scheme caused a tax loss to the IRS of at least $8 million.

    As one example from his investment scheme, in 2014, Bradford asked a client to make a $417,780 “investment” to his partnership entity in exchange for purported depreciation-based losses to be placed on his client’s 2013 corporate tax return (Form 1120S). But instead of reporting depreciation related to the investment, Bradford caused LLB to prepare and file a Form 1120S that falsely inflated the company’s cost of goods sold by $2,110,000, causing a tax loss to the IRS of approximately $860,627.

    In addition to the term of imprisonment, U.S. District Court Judge Gloria M. Navarro ordered Bradford to serve one year of supervised release and pay $6,734,338 in restitution to the United States.

    Acting Deputy Assistant Attorney General Stuart M. Goldberg of the Justice Department’s Tax Division and U.S. Attorney Jason M. Frierson for the District of Nevada made the announcement.

    IRS Criminal Investigation investigated the case, with assistance from the FBI.

    Trial Attorney Patrick Burns of the Tax Division and Assistant U.S. Attorney Steven W. Myhre for the District of Nevada prosecuted the case.

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    MIL Security OSI

  • MIL-OSI Security: Buffalo Man Pleads Guilty to Defrauding Lending Companies Out of Millions of Dollars with Dozens of Stolen Identities

    Source: US FBI

    ROCHESTER, N.Y. – U.S. Attorney Trini E. Ross announced today that Paul Paredes, 53, of Buffalo, NY, pleaded guilty before Chief U.S. District Judge Elizabeth A. Wolford to wire fraud and aggravated identity theft, which carry a mandatory minimum penalty of two years in prison, a maximum of 20 years, and a $250,000 fine.

    Assistant U.S. Attorney Katelyn M. Hartford, who is handling the case, stated that since 2013, Paredes has owned J&E Business Consulting LLC, which provides services to client businesses, including acting as a broker between the credit card processing companies and smaller retail businesses. Small businesses provide J&E with their financial information, such as the owner’s identifying information, including driver’s license, social security number, and email, as well as bank account information.

    Between April 2019, and August 2023, Paredes submitted hundreds of fraudulent financing applications to dozens of lenders under the identities of his business customers (victims), for goods and services allegedly provided by Paredes, with the funds payable to bank accounts controlled by Paredes. These financing agreements were prepared in the names if victims, using their personal information, and without their knowledge, consent, or authorization. After a financing agreement was approved, money from the victim lender was deposited into one of numerous bank accounts controlled by Paredes, who used the money he obtained from the scheme to fund his personal expenses, pay salaries and operating expenses, or to make payments to victim lenders to avoid detection of the fraudulent loans. By making those payments to the victim lenders, Paredes was able to continue making similar fraudulent financing agreements with multiple victim lenders without discovery. To further avoid detection, on occasions when victims received communications from victim lenders about the fraudulent financing agreements, Paredes misled them about the purpose of the communications and told them he would handle it.

    Paredes used the identity of at least 63 individuals to defraud at least 23 victim lenders, utilizing at least 12 different bank accounts and moving the money he obtained from the scheme throughout different accounts. Paredes submitted false loan applications from Rochester, NY, to the out-of-state lenders located throughout the country, including in New Jersey, Virginia, Illinois, Texas, North Carolina, Florida, Washington, Pennsylvania, Iowa, Colorado, and Minnesota. The total loss amount is at least $3,500,000.

    The plea is the result of an investigation by the Federal Bureau of Investigation, under the direction of Special Agent-in-Charge Matthew Miraglia, the Internal Revenue Service, under the direction of Special Agent-in-Charge Thomas Fattorusso, and the New York State Department of Financial Services-Criminal Investigation Bureau, under the direction of Superintendent Adrienne A. Harris.

    Sentencing is scheduled for April 29, 2025, at 2:30 p.m. before Judge Wolford.

    # # # #

    MIL Security OSI

  • MIL-OSI Security: U.S. Attorney’s Office Hosts Meeting of the Western District Health Care Fraud Working Group

    Source: US FBI

    Multi-Agency Partnership Continues Efforts to Combat Health Care Fraud and Protect Taxpayer Dollars

    CHARLOTTE, N.C. – U.S. Attorney Dena J. King announced today the annual meeting of the Western District’s Health Care Fraud Working Group, a partnership of federal and state agencies focused on combating health care fraud and protecting taxpayer dollars in the Western District of North Carolina.

    The working group comprises investigators, analysts, auditors, and attorneys from state and federal agencies, including the Federal Bureau of Investigation (FBI), the U.S. Department of Health and Human Services Office of Inspector General (HHS-OIG), the Food and Drug Administration’s Office of Criminal Investigations (FDA-OCI), the Internal Revenue Service Criminal Investigation (IRS-CI), the U.S. Department of Veterans Affairs Office of Inspector General (VA-OIG), the Department of Defense Office of Inspector General Defense Criminal Investigative Service (DCIS), the North Carolina Attorney General’s Medicaid Investigations Division, the North Carolina Department of Insurance, the South Carolina Medicaid Investigations Division, and the Office of Personnel Management.

    At today’s meeting, U.S. Attorney King reaffirmed the importance of collaboration among the partner agencies and recognized their contributions.

    “Health care fraud undermines public trust, exploits vulnerable patients, and siphons billions from taxpayer-funded programs,” said U.S. Attorney King. “By combining our expertise and resources we can detect, dismantle, and prosecute health care fraud schemes and protect vital government programs that so many North Carolinians rely upon for their health care needs. I am grateful to our partner agencies for their dedication to protect our health care system and hold perpetrators accountable.”

    The Health Care Fraud Working Group’s mission is to detect health care fraud through coordinated investigations, information sharing, identification of existing and emerging schemes, and case development. This includes uncovering schemes of fraudulent billing, COVID-19-related fraud, kickback schemes, and fraud targeting government health care programs like Medicare, Medicaid, and TRICARE. The working group also focuses on fraud committed by both corporate entities and individuals, including hospitals, telemedicine companies and providers, nursing home chains, pharmacies and pharmaceutical manufacturers, durable medical equipment suppliers, physicians, therapists, and affiliated health care professionals.

    If you suspect Medicare or Medicaid fraud, please report it by phone at 1-800-HHS-TIPS (1-800-447-8477), or via email at HHSTips@oig.hhs.gov.

    To report Medicaid fraud in North Carolina, call the North Carolina Medicaid Investigations Division at 919-881-2320 or fill out an online complaint form.

    TRICARE fraud can be reported here.

    Fraud against the U.S. Department of Veterans Affairs healthcare system can be reported at www.vaoig.gov/hotline.

    MIL Security OSI

  • MIL-OSI Security: BLM Activist Sentenced to Prison for Wire Fraud and Money Laundering

    Source: US FBI

    TOLEDO, Ohio – Sir Maejor Page, 35, of Toledo, has been sentenced to 42 months in prison by U.S. District Judge Jeffrey Helmick after a jury convicted him of wire fraud and money laundering for defrauding donors of more than $450,000 that they collectively gave to his nonprofit “Black Lives Matter of Greater Atlanta” (BLM of Greater Atlanta) based on Page’s false representations. He was also ordered to pay a $400 special assessment fee.

    Page continued to collect donations to his purported social justice charity through the organization’s Facebook page even after its tax-exempt status was revoked for failure to submit IRS Form 990 for three consecutive years.  He regularly posted content to Facebook about social and racial issues to give his nonprofit the appearance of legitimacy, despite no longer being tax-exempt. He also used Facebook to message privately with users, and he falsely represented that their donations would be used to “fight for George Floyd” and the “movement.” As a result, approximately 18,000 people donated to the BLM of Greater Atlanta charity through its Facebook account, which Page administered.

    Page used the donations to BLM for his own personal benefit. He purchased entertainment, hotel rooms, clothing, firearms, and a property that he intended to use as his personal residence. He attempted to conceal the purchase of the property by using the name “Hi Frequency Ohio” and asked the seller to sign a nondisclosure agreement that would have prevented the seller from listing Page as the actual buyer.

    “Mr. Page took advantage of a cause meant to fight social injustices, using it instead to line his own pockets with thousands of dollars of donations,” said U.S. Attorney Rebecca C. Lutzko for the Northern District of Ohio. “People donate their hard-earned money to support causes they believe in, and when a fraudster like Page comes along and tries to get away with a fake charity scheme, it hurts legitimate nonprofit organizations that rely on the generosity of others to advance their missions and make positive change in the world. This Office will hold accountable those who try to profit by scamming unsuspecting people out of their money like Page did here.”

    “The FBI will aggressively investigate individuals, like Sir Maejor Page, who engage in fraudulent charity schemes at the expense of the American public,” said FBI Cleveland Special Agent in Charge Greg Nelsen.  “Page is a calculating criminal who willingly conspired to steal hundreds of thousands of dollars through the trusting public. Today’s sentence holds him accountable and demonstrates that the FBI will steadfastly pursue perpetrators who target American citizens.”

    This case was investigated by the FBI Cleveland Division and prosecuted by Assistant U.S. Attorneys Gene Crawford and Rob Melching.

    MIL Security OSI

  • MIL-OSI USA: Kean Applauds Passage of the House Reconciliation Package

    Source: US Representative Tom Kean, Jr. (NJ-07)

    (May 22, 2025) WASHINGTON, D.C. — Today, Congressman Tom Kean, Jr. (NJ-07) released the following statement after the House passed its reconciliation package, a historic piece of legislation that delivers middle-class tax relief, unleashes American energy and innovation, and roots out waste, fraud, and abuse.

    Kean said, “We did it. The House just passed the Reconciliation package, a major step forward that delivers important wins for New Jerseyans and all Americans. I stood up for New Jersey every step of the way, even when it meant standing alone or standing against my own party’s leadership. I led the fight to restore our property tax deduction, and we won. The House bill restores the full SALT deduction for middle-class families, providing up to $40,000 in deductibility.

    “On healthcare, we protected Medicaid for every intended beneficiary in New Jersey and across the country and stopped illegal immigrants from stealing taxpayer-funded benefits. By rooting out waste, fraud, and abuse, we can ensure that this vital program is there for current and future generations of Americans. We also boosted the Child Tax Credit to $2,500, giving young families a much-needed return at a time when they need it most. We secured critical relief for Somerset and Morris Counties and the whole state of New Jersey by providing tens of millions of dollars for local and state law enforcement, ensuring they are supported while protecting President Trump over the next four years. We continued delivering for the American people by voting to secure our borders, unleash American energy and innovation, and invest in national security, all while cutting wasteful spending and making the federal government more efficient, accountable, and effective.

    “This bill lays the foundation for a stronger, more affordable America for middle-class families in the Seventh District, but the fight doesn’t stop here. I will continue advocating for the hardworking taxpayers in New Jersey until this bill reaches the President’s desk.”

     

    Key Wins in the House Reconciliation Package for New Jersey and the Nation:

    • SALT Deduction Raised: Raises the cap on the State and Local Tax deduction to $40,000, providing major relief for all middle-class families.

     

    • Medicaid Integrity Restored: Ensures benefits go only to eligible recipients and that those who are able to contribute to their community are doing so in order to receive Medicaid benefits. Preserves funding for New Jersey’s hospitals, nursing homes, and other providers.

     

    • Secret Service Reimbursement Secured: Secures vital federal support for local and state law-enforcement who provide protection when President Trump is at his home in Bedminster.

     

    • Border Security Strengthened: Provides resources to support border patrol agents, detect illegal drug smuggling, and secure our southern border.

     

    • American Energy Independence Advanced: Unleashes American energy production to help us meet our growing energy needs.

     

    • Child Tax Credit Boosted: Increased to $2,500, offering direct support for families after years of rising costs.

     

    • PBM Reform Achieved: Cracks down on abusive middlemen in the prescription drug market to lower costs for Medicare and consumers.

     

    • “Doc Fix” Enacted: Addresses long-standing Medicare physician payment issues to ensure that New Jersey’s doctors receive fair reimbursement for their important services.

     

    • Orphan Cures Act Passed: Eliminates a misguided law that slowed the development of drugs for patients with rare diseases. Many of these treatments are developed by New Jersey’s unparalleled biotech innovation industry.

     

    • Air Traffic Control Modernized: Delivers a $12.5 billion investment to overhaul, modernize, and staff our air traffic control system.

    ###

    MIL OSI USA News

  • MIL-OSI United Nations: Experts of the Committee on the Rights of the Child Praise Qatar’s Investments in Child Health and Education, Ask about the Age of Criminal Responsibility and Penalties for Child Offenders

    Source: United Nations – Geneva

    The Committee on the Rights of the Child today concluded its consideration of the fifth and sixth combined periodic reports of Qatar under the Convention on the Rights of the Child, with Committee Experts praising the State’s investments in child health and education, and raising questions about its efforts to raise the minimum age of criminal responsibility and prohibit the imposition of harsh penalties, including the death penalty and flagellation, on child offenders aged 16 years and over.

    Aissatou Alassane Sidikou, Committee Expert and Taskforce Coordinator for Qatar, commended Qatar’s efforts to invest in children’s health and education; implement its national development programme, which promoted sustainable development; establish its Ministry of Social Development and Family; and implement the Committee’s recommendations.

    Ms. Sidikou asked whether Qatar’s draft bill on children’s rights would increase the minimum age of criminal responsibility of children, which was currently one of the lowest in the world at seven years, and prohibit imprisonment, flagellation and forced labour for children, which was currently allowed from 16 years of age.  In Qatar, children could be sentenced to death. What measures were in place to strictly prohibit the application of the death penalty on children?

    Rosaria Correa, Committee Expert and Country Taskforce Member, said that despite the recommendations of various human rights mechanisms, the new nationality law did not allow Qatari women married to foreign citizens to pass on their nationality to their children. What steps had been taken to amend this law and other laws to allow Qatari women to pass on their nationality to their children?

    Introducing the report, Ahmad bin Hassan Al-Hammadi, Secretary-General of the Ministry of Foreign Affairs of Qatar and head of the delegation, said that, over the reporting period, Qatar had worked to strengthen legislative and institutional measures to protect children’s rights in the fields of education, health, social protection and criminal justice. The Qatar National Vision 2030 and the State’s third national development strategy 2024-2030 included key measures addressing children’s rights, and promoted equality and non-discrimination of children.

    The delegation said Qatar had reduced sentences for cases where perpetrators of crimes were children.  Sanctions for children under 16 years did not include corporal punishment or flagellation.  The draft law on the rights of the child would increase the minimum age of criminal liability and define all persons less than 18 years old as children.  It would be adopted and published soon.

    The delegation also said the death penalty could be imposed on children aged 16 to 18, who were more aware of their actions, but judges could commute the sentence, considering the age of the child when the crime was committed.  No one aged 16 to 18 had been sentenced to death in Qatar.

    The Qatari Nationality Code addressed the issue of kinship, the delegation said.  Children of non-Qatari fathers were given the nationality of their father, but such children also had the ability to access Qatari nationality if they had permanent residence.  The State had made great strides in reducing statelessness.

    In closing remarks, Ms. Sidikou said many efforts had been made by the State for children, but challenges remained.  The Committee hoped that the dialogue would help to improve protections for children in Qatar.

    Mr. Al-Hammadi, in concluding remarks, thanked the Committee and all persons who contributed to the constructive dialogue.  Qatar was committed to cooperating with the Committee and to addressing the challenges and risks it faced concerning the rights of the child.  It had achieved great progress in human rights over the years through cooperation with human rights mechanisms.

    Sophie Kiladze, Committee Chair, said in concluding remarks that the information provided by the State party would help the Committee to assess the achievements made by Qatar and the challenges it faced.  The Committee would do its best to develop concluding observations that would strengthen the rights of children in Qatar to the extent possible.

    The delegation of Qatar consisted of representatives from the Ministry of Foreign Affairs; Ministry of Interior; Ministry of Public Health; Ministry of Social Development and Family; Ministry of Education and Higher Education; Ministry of Justice; Supreme Judiciary Council; Public Prosecution; National Group for Protection of Children from Abuse and Violence; and the Permanent Mission of Qatar to the United Nations Office at Geneva.

    The Committee will issue the concluding observations on the report of Qatar at the end of its ninety-ninth session on 30 May. Those, and other documents relating to the Committee’s work, including reports submitted by States parties, will be available on the session’s webpage.  Summaries of the public meetings of the Committee can be found here, while webcasts of the public meetings can be found here.

    The Committee will next meet in public this afternoon at 3 p.m. to consider the combined fifth to seventh periodic reports of Brazil (CRC/C/BRA/5-7).

    Report

    The Committee has before it the fifth and sixth combined periodic reports of Qatar (CRC/C/QAT/5-6).

    Presentation of Report

    AHMAD BIN HASSAN AL-HAMMADI, Secretary-General of the Ministry of Foreign Affairs of Qatar and head of the delegation, said that Qatar was firmly and permanently committed to the principles of the Convention. Articles 21 and 22 of the Constitution emphasised the role of the family in protecting children from exploitation and neglect, and supporting their development.  The State had worked to strengthen legislative and institutional measures to protect children’s rights in the fields of education, health, social protection and criminal justice.

    The national report was the result of consultation and cooperation between the various national authorities, civil society and children.  The State had made great efforts to address and implement most of the previous recommendations made by the Committee, contributing to tangible progress in ensuring the rights of children.

    The Qatar National Vision 2030 and the State’s third national development strategy 2024-2030 included key measures addressing human rights issues in various fields, including children’s rights, and promoted equality and non-discrimination of children.  Over the reporting period, there had been extensive legislative amendments regarding the protection and promotion of children’s rights, most notably law 22 of 2021 regulating health care services, which included provisions promoting access to health care for all children, and the anti-cybercrime law, which criminalised sexual exploitation.  A draft law on children’s rights was also currently under review; it established effective mechanisms for the protection and development of children’s capacities and promoted the best interests of the child.

    The Ministry of Social Development and Family, established in 2021, was responsible for following up on childhood issues through specialised departments on family development, community welfare, and social protection.  The Qatar Foundation for Social Work had mechanisms for monitoring, follow-up and reporting on protection measures for child victims of violence, as well as awareness campaigns informing children of their rights and methods of reporting and seeking assistance.  The State had also established the National Planning Council, which was responsible for planning and implementing public policies related to children.  The Council of Ministers approved in April 2025 the establishment of the Digital Safety Committee for Children and Young People, and an awareness campaign on the safe use of technology would also be launched in June 2025.

    Efforts had continued to increase the enrolment rates of children, including children with disabilities, in compulsory education.  The overall enrolment rate was more than 97.5 per cent.  The State was encouraging girls to enrol in scientific disciplines; the percentage of girls in these disciplines had reached about 54 per cent at the secondary level.  New schools had also been established to provide technical and specialised education for both boys and girls.  The national education strategy 2024-2030 focused on improving the quality and inclusiveness of education, ensuring equal opportunities and enhancing governance. Five “peace schools” that received children of various nationalities, especially from countries in crisis, including children with disabilities, had been established.

    In the health sector, the national health strategy 2024-2030 was launched, which aimed to promote children’s health by preventing chronic diseases such as obesity and diabetes, and paying attention to oral health.  The State had established a system of child-friendly hospitals and general paediatric clinics.  The national team for child protection from violence and neglect received approximately 500 cases annually of suspected cases of child abuse and implemented preventive measures in response.  Effective countermeasures adopted during the COVID-19 pandemic contributed to Qatar having one of the lowest child mortality rates globally.

    Qatar’s Labour Code protected children from exploitation, prohibited their employment before reaching the legal age, and regulated the types of work that children could not do.  Moreover, the consumer protection law and the food control law promoted children’s rights as vulnerable consumers, while the Ministries of Health and Commerce were closely monitoring to ensure safe and healthy food for children.  The State had also launched plans to reduce and assess environmental pollution, especially in areas near schools and residential areas.

    The State had also paid attention to building the capacity of professionals working with children, such as judges, teachers, doctors and media professionals, through training programmes on the Convention delivered in cooperation with civil society.  Qatar was also studying the possibility of establishing a national children’s parliament and had established interactive platforms that allowed children to express their opinions and suggestions, especially when discussing policies that directly affected their lives.

    To protect children’s rights, Qatar was cooperating with United Nations agencies, including the United Nations Children’s Fund, which opened an office at the United Nations House in Doha in 2022. It was working to protect children in conflict areas in countries such as Syria, Palestine, Yemen, Somalia, Afghanistan, Russia and Ukraine.  The Qatari Education Above All initiative had reached over 17 million children in more than 65 countries.  Qatar had provided humanitarian assistance, including food and health care, to children in Gaza.

    Qatar was fully committed to the implementation of the Convention and its two Optional Protocols, and the protection of children’s rights.  Achieving this goal required continuous reform efforts through measures that kept pace with emerging changes and challenges.

    Questions by Committee Experts 

    AISSATOU ALASSANE SIDIKOU, Committee Expert and Taskforce Coordinator for Qatar, commended Qatar’s efforts to invest in children’s health and education; implement its national development programme, which promoted sustainable development; establish its Ministry on the Rights of Children and Families; and implement the Committee’s recommendations. Why had the State party maintained its reservations to articles two and 14 of the Convention?  The provisions in article two of the Convention were much broader than those of articles 34 and 35 of the Constitution. 

    Why was there was no schedule for adoption of the draft bill on children’s rights, which had been considered by the State for over 15 years?  Would the bill increase the minimum age of criminal responsibility of children, which was currently at seven years, and prohibit imprisonment, flagellation and forced labour for children, which was currently allowed from 16 years of age?  Did the National Human Rights Commission and the National Planning Council have sufficient resources?  How did they coordinate to protect child rights?

    Qatar’s investments in health and education had increased in 2022 and 2024, but these amounts were still below global standards.  Would this be addressed?  Were funds allocated for children in the budget clearly outlined?  How did the State party ensure that resources were equitably assigned?  A national survey conducted in 2023 contained very little information on vulnerable children. What was being done to strengthen data collection on such children?

    Did migrant children have access to mechanisms to report violations of their rights?  How did the State party support access to remedies for child victims? Were there capacity building and awareness raising mechanisms on child rights for State officials, civil society, the media and the public?  Did the National Human Rights Commission’s monitoring mechanism follow up on the implementation of the Convention and receive complaints on violations of the rights of children, including from migrant children?  How did the State party monitor policies and programmes on children’s rights?  Were there regulations that promoted compliance with international standards on children’s rights in the private sector?

    Girls in Qatar continued to face multiple forms of discrimination due to traditional beliefs.  What actions had been taken to change these negative social norms?  Children with disabilities, children with unmarried or foreign parents, and the children of migrant workers were subject to widespread discrimination.  How did the State party ensure that all children had access to basic social services?  Was there a general law prohibiting all forms of discrimination?

    There were no guidelines for professionals on determining the best interests of the child.  Would these be developed?  How did the State party ensure that this principle was applied consistently in all legal procedures?  In Qatar, children could be sentenced to death.  What measures were in place to strictly prohibit the application of the death penalty on children?  How did the State party facilitate the participation of children in matters affecting them?

    Despite the recommendations of various human rights mechanisms, the new nationality law did not allow Qatari women married to foreign citizens to pass on their nationality to their children. What steps had been taken to amend this law and other laws to allow Qatari women to pass on their nationality to their children?

    ROSARIA CORREA, Committee Expert and Taskforce Member, welcomed that the State party had taken several measures to address corporal punishment.  Had it assessed the impact that these measures had had on society? There was no law prohibiting corporal punishment.  What legislative efforts had been made to prohibit corporal punishment in all settings? Had studies into violent disciplining been carried out?  What measures had schools adopted to protect children?  How many child victims of violence had received remedies?  How was the State party monitoring child protection measures?  Did the draft bill on child rights address the child protection system?  Who was responsible for representing minors in the courts?

    How was the State party combatting the sale and trafficking of children domestically and internationally?  What was preventing the State from developing a law to ban child marriages?  How did the electronic monitoring system for convicted children work and how effective was it?  What social and psychological programmes were in place to protect the rights of children in conflict with the law and prevent their stigmatisation?

    TIMOTHY P.T. EKESA, Committee Expert and Taskforce Member, welcomed the data on children with disabilities that the State party had collected in 2016.  There were concerns that the State party did not provide access to mainstream education to all children with disabilities, as many were enrolled in special schools.  Only a small percentage of schools had inclusive education programmes, and a medical model was used to determine whether children with disabilities were enrolled in special schools.  Many children with disabilities remained out of school due to denial of admission or the inability of their families to pay school fees.  Could the State party provide data on the number of children with disabilities enrolled in mainstream education?

    Responses by the Delegation

    The delegation said its reservations to articles two and 14 of the Convention were consistent with Islamic Sharia and public morals.  The draft law on the rights of the child would increase the minimum age of criminal liability.  It would be adopted and published soon.

    In 2016, a programme was set up to investigate cases of violations of children’s rights and provide protection and remedies to victims.  It dealt with between 500 and 600 cases a year, some 30 per cent of which involved violence and negligence.  The programme included awareness raising campaigns on children’s rights and on reporting mistreatment of children.  A confidential hotline had been set up for reporting violence; it received 300 calls a year, 60 per cent of which came from children.  A register for cases of child abuse had recorded some 3,000 cases in recent years, and the Qatari Care Centre had provided psychological care to more than 4,000 children.  A conference on combatting violence against children held in 2020 in Qatar was attended by around 2,000 people.

    Qatar monitored the impact of business activities on children, guided by the United Nations Guiding Principles on Business and Human Rights.  The National Human Rights Committee monitored child labour but had not registered any cases. A regional conference had been held in Qatar that had called on businesses not to violate children’s rights in digital spaces.

    The Ministry of Social Affairs had signed a memorandum of understanding with the National Human Rights Committee on cooperation on protecting children’s rights.  This Committee was made up of eight representatives of civil society and five Government employees.  It reviewed legislation concerning children, visited schools to assess violations of children’s right to education, and conducted yearly awareness raising campaigns on the Convention.

    Qatari law did not permit marriages for boys under the age of 17 and girls under the age of 16.  Marriages under the age of 18 were permitted by judges only when there were exceptional circumstances.  A committee had been set up to review the Family Code; it was considering revising the legal minimum age of marriage.  It was very rare for families to allow their children to marry before the age of 18.

    Some six per cent of the national budget was allocated to education, and some 25 per cent of the Ministry of Social Affairs’ budget was allocated to programmes for children.  The State party had dispersed several million Qatari riyals for supporting vulnerable children and families.  A new centre for orphans was established in 2024.

    The Ministry of Education promoted gender equality at all stages of education.  Enrolment rates for boys and girls were equal at primary and secondary schools, and literacy rates were over 99 per cent in 2023.  The Ministry had launched awareness raising campaigns on human rights and non-discrimination.  Guidance was provided to teachers on preventing discrimination against children.  Qataris and non-Qataris received the same treatment in State schools and hospitals. Employers provided migrant workers with health insurance.

    The Qatari Nationality Code addressed the issue of kinship.  Children of non-Qatari fathers were given the nationality of their father, but such children also had the ability to access Qatari nationality if they had permanent residence.  The State had made great strides in reducing statelessness.

    Qatar had laws that enabled children to receive remedies such as compensation if they were victims of a crime. Specialised courts for crimes committed by children and cases of violence against children had been established, which could conduct hearings online.  There was also a witness protection programme for children. Courts had an interpretation and translation service that supported foreign children.  The State assigned lawyers to persons who could not afford them.

    All schools had student councils that allowed students to express their views on issues such as the environment, culture and education.  Cultural activities were organised for children.  Each school calculated its carbon footprint.

    Articles 21 and 68 of the Constitution incorporated the Convention into the legal order.  The State party had increased penalties for trafficking in persons when the victim was under 18 and reduced sentences for cases where perpetrators of crimes were children.  Sanctions for children under 16 years did not include corporal punishment, flagellation or the death penalty. 

    Articles permitting corporal punishment were removed from legislation after the adoption of the Convention. Persons, including parents, who used corporal punishment were held criminally liable.  Guidelines had been developed for parents on disciplining children without using corporal punishment and a centre that worked to educate parents on protecting children had been set up.  Corporal punishment in schools was banned in the 1990s. Inspectors conducted visits to schools to ensure that the rights of students were not violated. 

    The Prosecutor’s Office stepped in if there were conflicts of interest between parents and children.  Child psychologists were deployed to determine the best interests of the child.  Children’s confidentiality was protected in courts.

    The Ministry of Education attached great importance to inclusive education.  Curricula were adapted for children with disabilities and protocols had been adopted for children with autism.  There were programmes for vocational training for children with disabilities.

    Questions by Committee Experts

    ROSARIA CORREA, Committee Expert and Country Taskforce Member, said that Qatar had a set of measures to combat violence between children in schools.  Were there response measures and a recording mechanism for such violence? Some 83 per cent of children reportedly suffered from some form of harassment in primary school.

    What measures had been taken to ensure children could grow up in a pollution-free environment and access green spaces?  How did education programmes address climate change?  What impact was climate change having on Qatari children and how was the State working to mitigate its effects?  How was the State party encouraging children’s involvement in designing environmental policies?  How did the State party monitor children’s nutrition?

    How did the State party ensure that parents equally shared responsibilities concerning child-rearing? When parents divorced, the mother lost custody of her children in Qatar.  Were women who were victims of sexual exploitation criminalised in the Criminal Code?

    TIMOTHY P.T. EKESA, Committee Expert and Country Taskforce Member, said the national action plan on the inclusion of children with disabilities in schools had commendable objectives, but there was a lack of clarity on measures being implemented to achieve inclusion. Had the plan, which expired in 2023, been renewed?  Were there provisions in draft legislation on persons with disabilities that prohibited discrimination against children with disabilities in education?  The Committee had previously called on the State party to implement a national action plan on human rights education; had this been done?

    The Committee commended the State party’s high quality and widely accessible health care system and the launch of the national health strategy for 2023-2030.  Would children receive targeted attention under the strategy? There were reports of discrimination in access to health centres for non-Qatari citizens.  What measures were in place to address disparities in access to healthcare?  Qatar had one of the highest rates of adolescent obesity in the region.  How was the State party addressing this?  How was it promoting access to mental health for children and adolescents?

    BENOIT VAN KEIRSBILCK, Committee Expert and Country Taskforce Member, said that Qatar had not ratified the United Nations Educational, Scientific and Cultural Organization Convention against Discrimination in Education.  Why was this?  Why did most Qatari families choose private schools, while non-Qataris typically attended public schools?  What was the State party doing to support education costs?  There were schools that supported children who had dropped out of school; how effective were they?  Was there an official sexual and reproductive health education programme in schools? What was being done to promote access to safe and inclusive spaces for play and recreation?

    The Committee was concerned that Qatar continued to detain migrant children and families.  In which detention centres were migrants placed? Were there plans to revise the policy of detaining migrant children?  Most migrant workers in Qatar were men.  Were there plans to revise family reunification rules to make it more accessible for workers with low wages?  Were there plans to regularise the children of migrants born in Qatar?

    Members of the Al-Ghufran clan had been deprived of their nationality many years ago. How many of these people still did not have Qatari nationality, and were there plans to resolve their situation? How did the State party ensure that migrant children could enrol in schools and how did it investigate complaints issued by domestic workers?  How many girls were working as domestic workers?  What programmes were in place that supported children in street situations? What results had been achieved by the law on trafficking in persons?  What measures had been implemented to prevent and prosecute cases of trafficking in children occurring during the 2022 World Cup?

    Qatar had one of the lowest minimum ages of criminal responsibility in the world, at seven years of age, and many legal protections for child offenders only applied for children under age 16.  How many children up to 18 years old were deprived of liberty and in what settings? Were they mixed with adults?  Were children in detention informed about the National Human Rights Committee’s complaints mechanism?  Did the State party intend to ratify the Safe Schools Declaration?

    Responses by the Delegation

    The delegation said corporal punishment against all persons was prohibited, including punishment of persons with disabilities.  There was no dedicated legislation on domestic violence, but there were legislative measures that covered domestic violence, and a court had been set up that specialised in domestic violence and temporary shelters, mandated to protect women and children who were victims of domestic violence.  In 2024, the State party organised workshops training for around 5,000 people on issues such as protecting children from violence and intimidation.  There were around 40,000 confirmed cases of domestic violence between 2024 and 2025.

    Initiatives had been adopted to minimise the impact of climate change on children, including adaption of infrastructure and measures to reduce carbon emissions and increase the use of renewable energy.  The State party had constructed 18 square kilometres of green zones in 2023 and an additional eight in 2024.  There was also a course within the school curriculum that focused on protecting the environment and living sustainably.  Schools celebrated a “sustainability week”.  Qatar had also taken measures to ensure the provision of good quality water.  It periodically monitored water and air quality in schools, kindergartens and public hospitals. 

    Qatar promoted children’s health through various measures.  Nine free health check-ups were provided to children up to age five.  The State party encouraged exclusive breastfeeding up to six months; there had been a sharp increase in breastfeeding rates over the past decade.  The State party had developed programmes to tackle the child obesity rate, which aimed to reduce this rate by 30 per cent by 2030.  School nutrition clinics provided specialised services to prevent childhood obesity and nutritional problems.  A 2022 law governed universal healthcare coverage.

    Sexual and reproductive health education and education on drug addiction were provided in schools from primary level, and there was also teaching on the protection of children from neglect, and online and sexual exploitation.  Children were instructed on how to find psychological assistance, and on alerting authorities about threats.

    Qatar promoted access to a healthy environment for children with disabilities.  It had beaches that had been adapted to ensure accessibility.  Various projects were being developed for children with disabilities up to 2030.  A single database covering all children with disabilities in the education system had been set up.  Qatar had over 5,300 pupils with disabilities in public and private schools.  Some 62 per cent of schools were inclusive. There were specialised training programmes for children with disabilities that supported them to become autonomous.

    Children with disabilities had access to specialised healthcare through 10 healthcare centres tailored to their needs, including four centres for children with autism.  The third national strategy 2024-2030 included measures for improving rehabilitation and diagnosis services for persons with disabilities. Social workers, family and community members were trained to care for children with disabilities and support their inclusion in society. 

    Qatari legislators sought to recognise children with disabilities as having legal capacity on par with others, and to promote their access to work, education and other rights.  The draft disability code had been developed and was now being deliberated by the Government.  Measures to exempt persons with disabilities from certain Government fees were being developed.  Legislators sought to promote access to complaints mechanisms for children with disabilities and their families.  The State funded legal aid services to support children in court, including children with disabilities.

    The draft child code defined all persons less than 18 years old as children.

    As part of the 2024-2030 development strategy, the State party had visited schools and engaged in dialogue with students, parents and teachers.  “Sustainability ambassadors” who promoted environmental protection were appointed in schools, and young people could contribute to the Shura Council. Many children had taken part in drafting the State party’s report.

    The State party was promoting awareness of human rights for children through social education courses and campaigns in schools, through which children learned about the Convention, gender equality, democracy, acceptance of others, cybersecurity, and preventing bullying.  Media campaigns on children’s rights were carried out and manuals and training programmes had been developed to inform teachers, social workers and other public officials about children’s rights.  The State party organised annual events to mark Children’s Day.

    Qatar was committed to protecting school establishments from attack.  It had signed the Safe Schools Declaration and participated in the Education for All initiative.  Qatar helped organise events on 9 September each year at United Nations offices in New York and Geneva to mark the International Day to Protect Education from Attack.

    Public schools applied international standards, including the international baccalaureate programme. Migrant parents could choose the school that their children attended and the language of instruction.  The State ensured the provision of free schooling to students coming from regions of armed conflict.

    Questions by Committee Experts

    BENOIT VAN KEIRSBILCK, Committee Expert and Country Taskforce Member, asked whether police provided sexual education in schools?  Was legal aid free for every child and accessible from the first stage of arrest? Did the State party criminally prosecute children who were addicted to drugs?

    TIMOTHY P.T. EKESA, Committee Expert and Country Taskforce Member, said Qatar generally prohibited abortion, only allowing it in three special cases.  There were severe penalties imposed on women who received unauthorised abortions.  How many unauthorised abortions had the State recorded over the reporting period?

    Another Committee Expert asked about the likelihood of approving the children’s act soon.  Would Qatar provide a complete definition of the child in this legislation?

    A Committee Expert asked about awareness raising campaigns in place to reduce the rate of child deaths from road accidents, which remained quite high in Qatar.  How was wastewater treated and what percentage of the population had access to potable water?

    One Committee Expert asked if Qatari children had access to contraception.  Were children who were the product of rape given Qatari nationality? Did national institutions take a gender specific approach?  Was free legal assistance provided to victims of domestic violence?

    A Committee Expert asked about the level of integration that the State party’s hotline had with law enforcement, health services and social services.  What services were provided to children of adults deprived of liberty, including adults on death row?

    SOPHIE KILADZE, Committee Chair, asked whether the State party had measures to reduce children’s screen time and a policy on artificial intelligence and its effects on children.

    Responses by the Delegation

    The delegation said the 2015 law on the departure of migrants set up a mechanism for entering and exiting Qatar. It regulated the provision of housing, healthcare and education for migrants, as well as the conditions migrants needed to meet to obtain residence permits.  Migrants who did not meet these conditions were deported following the standard procedure.  Persons without identity documents who were accompanied by children, as well as stateless and unaccompanied children, were placed in a shelter while being processed. In 2024, there were 22 such detentions, and thus far there had been six detentions in 2025.  The State party worked with relevant embassies to support processing of these people.

    A directorate had been established that was mandated to prevent road accidents.

    Psychological support was provided to children whose parents had been sentenced to death.  The Criminal Procedural Code provided for two years of reprieve from detention for pregnant women, and when both parents were charged with the same crime, one parent was granted reprieve from detention to care for their children while the other parent was detained.

    The age of criminal liability started from seven years.  From ages seven to 16, judges could only impose sanctions requiring the child’s parents to obey certain commitments or send the child to rehabilitation programmes. The juvenile justice system was based on rehabilitation, not punishment.  Children aged 16 to 18 were more aware of their actions and thus had increased criminal liability.  The death penalty could be used on such children, but judges could commute the sentence, considering the age of the child when the crime was committed.  No one aged 16 to 18 had been sentenced to death in Qatar.

    Qatar had evacuated over 65,000 people from Afghanistan in 2021.  Qatar provided these people with housing and psychological support and facilitated their voluntary travel to other countries.  The State had also evacuated many children from Gaza to Qatar, providing them with free healthcare and education.

    Sexual education was provided by teachers and social workers, not police, in schools.  A national workshop had been set up to develop sexual education; psychologists were involved in this process.

    The State had a legal aid office with attorneys who provided children with free legal assistance and defended them in court.  The office also provided assistance in cases of domestic violence.

    Islamic Sharia was the source of laws in Qatar.  Criminal legislation on abortion was in line with Sharia.  In the State’s view, foetuses had the same rights as adults and benefited from legal protection.  Abortions could only take place if the pregnancy threatened the life of the mother.  Children who were the product of rape could access Qatari nationality.

    Qatar had created legislation combatting cybercrime, which punished all digital intimation and threats.  There were harsher sentences when the victim was a child or had a disability.  The State had also launched a platform that aimed to educate children and families on the safe use of digital technology and build children’s digital skills.  It had a national strategy on artificial intelligence and was committed to developing digital infrastructure that respected human rights. 

    Qatar had acceded to International Labour Organization Conventions 138 and 180 on child labour.  The State’s law on domestic workers protected such workers from exploitation.  The law banned hiring people under 18 years of age for domestic work.  Migrant workers needed to be 18 years of age or older. Domestic workers had the same rights as other workers, including regarding access to healthcare.  There was a Government Department that received complaints from domestic workers, which operated in 11 different languages.

    The State party respected the rights of migrant workers to live with their families.  These workers could bring their children to the State if they fulfilled a strict set of conditions.

    Qatar had criminalised all forms of trafficking of persons, including labour exploitation.  Penalties for trafficking were increased when the victim was a child.  There was a committee within the Ministry of Labour that was responsible for combatting trafficking in persons.  Qatari law was in line with the Optional Protocol on the sale of children, child prostitution and child pornography.

    The hotline for reporting violations of children’s rights was manned by psychologists, who assessed the urgency of the complaint and referred it to the relevant authorities.

    The Qatar Social Work Foundation worked to enhance family bonds and to prevent domestic violence.  It provided lectures for prospective parents and counselling and mediation services seeking to resolve family problems amicability. The Foundation worked to defend children’s rights in cases of divorce, providing them with psychological counselling. Legislation had been developed that ensured that custody could be provided to mothers in cases of divorce.

    Concluding Remarks 

    AISSATOU ALASSANE SIDIKOU, Committee Expert and Taskforce Coordinator, thanked the delegation for the interesting dialogue.  Many efforts had been made by the State for children, but challenges remained.  The Committee hoped that the dialogue would help to improve protections for children in Qatar.  Ms. Sidikou said she hoped that the members of the State party would carry all children in their hearts in their work.

    AHMAD BIN HASSAN AL-HAMMADI, Secretary-General of the Ministry of Foreign Affairs of Qatar and head of the delegation, thanked the Committee and all persons who had contributed to the constructive dialogue, which was an important opportunity to promote the rights of the child and global peace.  The State party would use the Committee’s concluding observations to improve measures for children.  The Committee needed to consider the information provided by the State and its cultural specificities.  Qatar was committed to cooperating with the Committee and to addressing the challenges and risks it faced concerning the rights of the child.  It had achieved great progress in human rights over the years through cooperation with human rights mechanisms.

    SOPHIE KILADZE, Committee Chair, said that the information provided by the State party would help the Committee to assess the achievements made by Qatar and the challenges it faced. The Committee respected States’ cultural specificities, but violations of the Convention could not be justified in any circumstances.  The Committee would do its best to develop concluding observations that would strengthen the rights of children in Qatar to the extent possible.  It hoped that the State party would present further progress for children in its next dialogue with the Committee.

    ___________

    Produced by the United Nations Information Service in Geneva for use of the media; 
    not an official record. English and French versions of our releases are different as they are the product of two separate coverage teams that work independently.

     

    CRC25.014E

    MIL OSI United Nations News

  • MIL-OSI USA: AI Data Security: Best Practices for Securing Data Used to Train & Operate AI Systems

    News In Brief – Source: US Computer Emergency Readiness Team

    Executive summary

    This Cybersecurity Information Sheet (CSI) provides essential guidance on securing data used in artificial intelligence (AI) and machine learning (ML) systems. It also highlights the importance of data security in ensuring the accuracy and integrity of AI outcomes and outlines potential risks arising from data integrity issues in various stages of AI development and deployment.

    This CSI provides a brief overview of the AI system lifecycle and general best practices to secure data used during the development, testing, and operation of AI-based systems. These best practices include the incorporation of techniques such as data encryption, digital signatures, data provenance tracking, secure storage, and trust infrastructure. This CSI also provides an in-depth examination of three significant areas of data security risks in AI systems: data supply chain, maliciously modified (“poisoned”) data, and data drift. Each section provides a detailed description of the risks and the corresponding best practices to mitigate those risks. 

    This guidance is intended primarily for organizations using AI systems in their operations, with a focus on protecting sensitive, proprietary, or mission critical data. The principles outlined in this information sheet provide a robust foundation for securing AI data and ensuring the reliability and accuracy of AI-driven outcomes.

    This document was authored by the National Security Agency’s Artificial Intelligence Security Center (AISC), the Cybersecurity and Infrastructure Security Agency (CISA), the Federal Bureau of Investigation (FBI), the Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC), the New Zealand’s Government Communications Security Bureau’s National Cyber Security Centre (NCSC-NZ), and the United Kingdom’s National Cyber Security Centre (NCSC-UK). 

    The goals of this guidance are to: 

    1. Raise awareness of the potential risks related to data security in the development, testing, and deployment of AI systems;
    2. Provide guidance and best practices for securing AI data across various stages of the AI lifecycle, with an in-depth description of the three aforementioned significant areas of data security risks; and
    3. Establish a strong foundation for data security in AI systems by promoting the adoption of robust data security measures and encouraging proactive risk mitigation strategies.

    Download the PDF version of this report: 

    Introduction

    The data resources used during the development, testing, and operation of an AI1 system are a critical component of the AI supply chain; therefore, the data resources must be protected and secured. In its Data Management Lexicon, [1] the Intelligence Community (IC) defines Data Security as “The ability to protect data resources from unauthorized discovery, access, use, modification, and/or destruction…. Data Security is a component of Data Protection.” 

    Data security is paramount in the development and deployment of AI systems. Therefore, it is a key component of strategies developed to safeguard and manage the overall security of AI-based systems. Successful data management strategies must ensure that the data has not been tampered with at any point throughout the entire AI system lifecycle; is free from malicious, unwanted, and unauthorized content; and does not have unintentional duplicative or anomalous information. Note that AI data security depends on robust, fundamental cybersecurity protection for all datasets used in designing, developing, deploying, operating, and maintaining AI systems and the ML models that enable them.

    Audience and scope

    This CSI outlines potential risks in AI systems stemming from data security issues that arise during different phases of an AI deployment, and it introduces recommended protocols to mitigate these risks. This guidance builds upon the NSA’s joint guidance on Deploying AI Systems Securely [2] and delves deeper into securing the data used to train and operate AI-based systems. This guidance is primarily developed for organizations that use AI systems in their day-to-day operations, including the Defense Industrial Base (DIB), National Security System (NSS) owners, Federal Civilian Executive Branch (FCEB) agencies, and critical infrastructure owners and operators. Implementing these mitigations can help secure AI-enabled systems and protect proprietary, sensitive, and/or mission critical data.

    Securing data throughout the AI system lifecycle

    Data security is a critical enabler that spans all phases of the AI system lifecycle. ML models learn their decision logic from data, so an attacker who can manipulate the data can also manipulate the logic of an AI-based system. In the AI Risk Management Framework (RMF) [3], the National Institute of Standards and Technology (NIST) defines six major stages in the lifecycle of AI systems, starting from Plan & Design and progressing all the way to Operate & Monitor. The following table highlights relevant data security factors for each stage of the AI lifecycle: 

    Table 1: The AI System Lifecycle with key dimensions, necessary ongoing assessments, focus areas for data security, and particular data security risks covered in this CSI. [3] 
    AI Lifecycle Stage Key Dimensions Test, Evaluation, Verification, & Validation (TEVV) Potential Focus Areas for Data Security Particular Data Security Risks Covered in this CSI
    1) Plan & Design Application Context Audit & Impact Assessment Incorporating data security measures from inception, designing robust security protocols, threat modeling, and including privacy by design Data supply chain
    2) Collect & Process Data Data & Input Internal & External Validation Ensuring data integrity, authenticity, encryption, access controls, data minimization, anonymization, and secure data transfer Data supply chain,
    maliciously modified data
    3) Build & Use Model AI Model Model Testing Protecting data from tampering, ensuring data quality and privacy (including differential privacy and secure multi-party computation when appropriate and possible), securing model training, and operating environments   Data supply chain,
    maliciously modified data
    4) Verify & Validate AI Model Model Testing Performing comprehensive security testing, identifying and mitigating risks, validating data integrity, adversarial testing, and formal verification when appropriate and possible Data supply chain,
    maliciously modified data
    5) Deploy & Use Task & Output Integration, Compliance Testing, Validation Implementing strict access controls, zero-trust infrastructure, secure data transmission and storage, secure API endpoints, and monitoring for anomalous behavior Data supply chain,
    maliciously modified data,
    data drift
    6) Operate & Monitor Application Context Audit & Impact Assessment Conducting continuous risk assessments, monitoring for data breaches, deleting data securely, complying with regulations, incident response planning, and regular security auditing Data supply chain,
    maliciously modified data, data drift

    Throughout the AI system lifecycle, securing data is paramount to maintaining information integrity and system reliability. Starting with the initial Plan & Design phase, carefully plan data protection measures to provide proactive mitigations of potential risks. In the Collect & Process Data phase, data must be carefully analyzed, labeled, sanitized, and protected from breaches and tampering. Securing data in the Build & Use Model phase helps ensure models are trained on reliably sourced, accurate, and representative information. In the Verify & Validate phase, comprehensive and thorough testing of AI models, derived from training data, can identify security flaws and enable their mitigation. 

    Note that Verification & Validation is necessary each time new data or user feedback is introduced into the model; therefore, that data also needs to be handled with the same security standards as AI training data. Implementing strict access controls protects data from unauthorized access, especially in the Deploy & Use phase. Lastly, continuous data risk assessments in the Operate & Monitor phase are necessary to adapt to evolving threats. Neglecting these practices can lead to data corruption, compromised models, data leaks, and non-compliance, emphasizing the critical importance of robust data security at every phase.

    Best practices to secure data for AI-based systems

    The following list contains recommended practical steps that system owners can take to better protect the data used to build and operate their AI-based systems, whether running on premises or in the cloud. For more details on general cybersecurity best practices, see also NIST SP 800-53, “Security and Privacy Controls for Information Systems and Organizations.” [33]

    1. Source reliable data and track data provenance
    Verify data sources use trusted, reliable, and accurate data for training and operating AI systems. To the extent possible, only use data from authoritative sources. Implement provenance tracking to enable the tracing of data origins, and log the path that data follows through an AI system. [7],[8],[9] Incorporate a secure provenance database that is cryptographically signed and maintains an immutable, append-only ledger of data changes. This facilitates data provenance tracking, helps identify sources of maliciously modified data, and helps ensure that no single entity can undetectably manipulate the data.
    2. Verify and maintain data integrity during storage and transport
    Maintaining data integrity2 is an essential component to preserve the accuracy, reliability, and trustworthiness of AI data. [4] Use checksums and cryptographic hashes to verify that data has not been altered or tampered with during storage or transmission. Generating such unique codes for AI datasets enables the detection of unauthorized changes or corruption, safeguarding the information’s authenticity.

    3. Employ digital signatures to authenticate trusted data revisions
    Digital signatures help ensure data integrity and prevent tampering by third parties. Adopt quantum-resistant digital signature standards [5][6] to authenticate and verify datasets used during AI model training, fine tuning, alignment, reinforcement learning from human feedback (RLHF), and/or other post-training processes that affect model parameters. Original versions of the data should be cryptographically signed, and any subsequent data revisions should be signed by the person who made the change. Organizations are encouraged to use trusted certificate authorities to verify this process.
    4. Leverage trusted infrastructure
    Use a trusted computing environment that leverages Zero Trust architecture. [10] Provide secure enclaves for data processing and keep sensitive information protected and unaltered during computations. This approach fosters a secure foundation for data privacy and security in AI data workflows by isolating sensitive operations and mitigating risks of tampering. Trusted computing infrastructure supports the integrity of data processes, reduces risks associated with unverified or altered data, and ultimately creates a more robust and transparent AI ecosystem. Trusted environments are essential for AI applications where data accuracy directly impacts their decision-making processes.
    5. Classify data and use access controls
    Categorize data using a classification system based on sensitivity and required protection measures. [11] This process enables organizations to apply appropriate security controls to different data types. Classifying data enables the enforcement of robust protection measures like stringent encryption and access controls. [33] In general, the output of AI systems should be classified at the same level as the input data (rather than creating a separate set of guardrails).
    6. Encrypt data
    Adopt advanced encryption protocols proportional to the organizational data protection level. This includes securing data at rest, in transit, and during processing. AES-256 encryption is the de facto industry standard and is considered resistant to quantum computing threats. [12],[13] Use protocols, such as TLS with AES-256 or post-quantum encryption, for data in transit. Refer to NIST SP 800-52r2, “Guidelines for the Selection, Configuration, and Use of Transport Layer Security (TLS) Implementations” [14] for more details.
    7. Store data securely
    Store data in certified storage devices that enforce NIST FIPS 140-3 [15] compliance, ensuring that the cryptographic modules used to encrypt the data provide high-level security against advanced intrusion attempts. Note that Security Level 3 (defined in NIST FIPS 140-2 [16]) provides robust data protection; however, evaluate and determine the appropriate level of security based on organizational needs and risk assessments.
    8. Leverage privacy-preserving techniques 
    There are several privacy-preserving techniques [17] that can be leveraged for increased data security. Note that there may be practical limitations to their implementation due to computational cost.

    • Data depersonalization techniques (e.g., data masking [18]) involve replacing sensitive data with inauthentic but realistic information that maintains the distributions of values throughout the dataset. This enables AI systems to utilize datasets without exposing sensitive information, reducing the impact of data breaches and supporting secure data sharing and collaboration. When possible, use data masking to facilitate AI model training and development without compromising sensitive information (e.g., personally identifiable information [PII]).
    • Differential privacy is a framework that provides a mathematical guarantee quantifying the level of privacy of a dataset or query. It requires a pre-specified privacy budget for the level of noise added to the data, but there are tradeoffs between protecting the training data from membership inference techniques and target task accuracy. Refer to [17] for further details.
    • Decentralized learning techniques (e.g., federated learning [19]) permit AI system training over multiple local datasets with limited sharing of data among local instances. An aggregator model incorporates the results of the distributed models, limiting access on the local instance to the larger training dataset. Secure multi-party computation is recommended for training and inferencing processes.

    9. Delete data securely
    Prior to repurposing or decommissioning any functional drives used for AI data storage and processing, erase them using a secure deletion method such as cryptographic erase, block erase, or data overwrite. Refer to NIST SP 800-88, “Guidelines for Media Sanitization,” [20] for guidance on appropriate deletion methods.
    10. Conduct ongoing data security risk assessments
    Conduct ongoing risk assessments using industry-standard frameworks, such as the NIST SP 800-3r2, Risk Management Framework (RMF) [4][21], and the NIST AI 100-1, Artificial Intelligence RMF [3]. These assessments should evaluate the AI data security landscape, identify risks, and prioritize actions to minimize security incidents. Continuously improve data security measures to keep pace with evolving threats and vulnerabilities, learn from security incidents, stay up to date with emerging technologies, and maintain a robust security posture. 

    Data supply chain – risks and mitigations

    Relevant AI Lifecycle stages: 1) Plan & Design; 2) Collect & Process Data; 3) Build & Use Model; 4) Verify & Validate; 5) Deploy & Use; 6) Operate & Monitor

    Developing and deploying secure and reliable AI systems requires understanding potential risks and methods of introducing inaccurate or maliciously modified (a.k.a. “poisoned”) data into the system. In short, the security of AI systems depends on thorough verification of training data and proactive measures to detect and prevent the introduction of inaccurate material.

    Threats can stem from large-scale data collected and curated by third parties, as well as from data that is not sufficiently protected after ingestion. Data collected and/or curated by a third party may contain inaccurate information, either unintentionally or through malicious intent. Inaccurate material can compromise not only models trained using that data, but also any additional models that rely on compromised models as a foundation.  

    It is crucial, therefore, to verify the integrity of the training data used when building an AI system. Organizations that utilize third-party data must take appropriate measures to ensure that: 1) the data is not compromised upon ingestion; and 2) the data cannot be compromised after it has been incorporated into the AI system. As such, both data curators and data consumers should follow the best practices for digital signatures, data integrity, and data provenance that are described in detail above.

    General risks for data consumers3 

    The use of web-scale databases includes all of the risks outlined earlier, and one cannot simply assume that these datasets are clean, accurate, and free of malicious content. Third-party models trained on web-scraped data used to train a model for downstream tasks could also affect the model’s learning process and result in behavior that was unintended by the AI system designer.

    From the moment data is ingested for use with AI systems, the data acquirer must secure it against insider threats and malicious network activity to prevent unauthorized modification. 

    Mitigation strategies: 

    • Dataset verification: Before ingest, the consumer or curator should verify, as much as possible, that the dataset to be ingested is free of malicious or inaccurate material. Any detected abnormalities should be addressed, and suspicious data should not be stored. The dataset verification process should include a digital signature of the dataset at time of ingestion.
    • Content credentials: Use content credentials to track the provenance of media and other data. Content credentials are “metadata that are secured cryptographically and allow creators the ability to add information about themselves or their creative process, or both, directly to media content…. Content Credentials securely bind essential metadata to a media file that can track its origin(s), any edits made, and/or what was used to create or modify the content…. This metadata alone does not allow a consumer to determine whether a piece of content is ‘true,’ but rather provides contextual information that assists in determining the authenticity of the content.” [24]
    • Foundation model assurances: In the case where a consumer is not ingesting a dataset but a foundation model trained by another party, the developers of the foundation model need to be able to provide assurances regarding the data and sources used and certify that their training data did not contain any known compromised data. Take care to track the training data used in various model lineages. Exercise caution before using a model without such assurances.
    • Require certification: Data consumers should strongly consider requiring a formal certification from dataset and model providers, attesting that their systems are free from known compromised data before using third-party data and/or foundation models.
    • Secure storage: After ingest, data needs to be stored in a database that adheres to the best practices for digital signatures, data integrity, and data provenance that are described in detail above. Note that an append-only cryptographically signed database should be used where feasible, but there may be a need to delete older material that is no longer relevant. Each time a data element is updated (e.g., resized, cropped, flipped, etc.) for augmentation purposes in a non-temporary fashion, then the updated data should be stored as a new entry with documented changes. The database’s certificate should be verified at the time the database is accessed for a training run. If the database does not pass the certificate check, abort the training and conduct a comprehensive database audit to discover any data modifications. 

    2023 investigations by various industry professionals explored low-resource methods for introducing malicious or inaccurate material into web-scale datasets, and potential strategies to mitigate this risk.  [29] These vulnerabilities depend on the fact that curators or collectors do not have control over the data, as seen in cases of datasets curated by third parties (e.g., LAION) or datasets that are continually updated and released (e.g., Wikipedia). 

    Risk: Curated web-scale datasets

    Curated AI datasets (e.g., LAION-2B or COYO-700M) are vulnerable to a type of technique known as split-view poisoning. This risk arises because these datasets often contain data hosted on domains that may have expired or are no longer actively maintained by their original owners. In such cases, anyone who purchases these expired domains gains control over the content hosted on them. This situation creates an opportunity for malicious actors to modify or replace the data that the curated list points to, potentially introducing inaccurate or misleading information into the dataset. In many instances, it is possible to purchase enough control of a dataset to conduct effective poisoning for roughly $1,000 USD. In some cases, effective techniques can cost as little as $60 USD (e.g., COYO-700M), making this a viable threat from low-resource threat actors. 

    Mitigation strategies:

    • Raw data hashes: Data curators should attach a cryptographic hash to all raw data referenced in the dataset. This will enable follow-on data consumers to verify that the data has not changed since it was added to the list.
    • Hash verification: Data consumers should incorporate a hash check at time of download in order to detect any changes made to it, and the downloader should discard any data that does not pass the hash check.
    • Periodic checks: Curators should periodically scrape the data themselves to verify that the data has not been modified. If any changes are detected, the curator should take appropriate steps to ensure the data’s integrity.
    • Verifying data: Curators should verify that any changed data is clean and free from inaccurate or malicious material. If the content of the data has been altered in any way, the curator should either remove it from their list or flag it for further review.
    • Certification by curators: Since the data supply chain begins with the curators, the certification process must start there as well. To the best of their ability, curators should be able to certify that, at the time of publication, the dataset contains no malicious or inaccurate material. 

    Risk: Collected web-scale datasets

    Collected web-scale datasets (e.g., Wikipedia) are vulnerable to frontrunning poisoning techniques. Frontrunning poisoning occurs when an actor injects malicious examples in a short time window before websites with crowd-sourced content collect a snapshot of their data. Wikipedia in particular conducts twice-monthly snapshots of their data and publishes these snapshots for people to download. Since the snapshots happen at known times, it is possible for malicious actors to edit pages close enough to the snapshot time so that malicious edits will be captured and published before they can be discovered and corrected. Industry analysis demonstrated potential malicious actors would be able to successfully poison as much as 6.5% of Wikipedia. [29]

    Mitigation strategies:

    • Test & verify web-scale datasets: Be cautious when using web-scale datasets that are vulnerable to frontrunning poisoning. Check that the data hasn’t been manipulated, and only use snapshots verified by a trusted party.
    • (For web-scale data collectors) Randomize or lengthen snapshots: Collectors such as Wikipedia should defend against actors making malicious edits ahead of a planned snapshot by:
    1. Randomizing the snapshot order.
    2. Freezing edits to content long enough for edits to go through review before releasing the snapshot.

      These mitigations focus on increasing the amount of time a malicious actor must maintain control of the data for it to be included in the published snapshot. Any reasonable methods that increase the time a malicious actor must control the data are also recommended. 

      Note that these mitigations are limited since they rely on trusted curators who can detect malicious edits. It is more difficult to defend against subtle edits (e.g., attempts to insert hidden watermarks) that appear valid to human reviewers but impact machine understanding.

    Risk: Web-crawled datasets 

    Web-crawled datasets present a unique intersection of the risks discussed above. Since web-crawled datasets are substantially less curated than other web-scale datasets, they bring increased risk. There are no trusted curators to detect malicious edits. There are no original curated views to which cryptographic hashes can be attached. The unfortunate reality is that “updates to a web page have no realistic bound on the delta between versions which might act as a signal for attaching trust.” [29]

    Mitigation strategies:

    • Consensus approaches: Data consumers using web-crawled datasets should rely on consensus-based approaches, since notional determinations of which domains to trust are ad-hoc and insufficient. For example, an AI developer could choose to only trust an image-caption pair when it appears on many different websites to reduce susceptibility to poisoning techniques, since a malicious actor would have to poison a sufficiently large number of websites to be successful.
    • Data curation: Ultimately, it is incumbent on organizations to ensure malicious or inaccurate material is not present in the data they use. If an organization does not have resources to conduct the necessary due diligence, then the use of web-crawled datasets is not recommended until some sort of trust infrastructure can be implemented.

    Final note on web-scale datasets and data poisoning

    Both split-view and frontrunning poisoning are reasonably straightforward for a malicious actor to execute, since they do not require particularly sophisticated methodology. These poisoning techniques should be considered viable threats by anyone looking to incorporate web-scale data into their AI systems. The danger here comes not only from directly using compromised data, but also from using models which may themselves have been trained on compromised data. 

    Ultimately, data poisoning must be addressed from a supply chain perspective by those who train and fine-tune AI models. Proper supply chain integrity and security management (i.e., selecting reliable model providers and verifying the legitimacy of the models used) can reduce the risk of data poisoning and system compromise. The most reliable providers are those who assure that they do everything possible to prevent the influence and distribution of poisoned data and models. [34] 

    Every effort must be made by those building foundation models to filter out malicious and inaccurate data. Foundation models are evolving rapidly, and filtering out inaccurate, unauthorized, and malicious training data is an active area of research, particularly at web-scale. As such, is currently impractical to prescribe precise methods for doing so; it is a best-effort endeavor. Ideally, data curators and foundation model providers should be able to attest to their filtering methods and provide evidence (e.g. test results) of their effectiveness. Likewise, if possible, downstream model consumers should include a review of the security claims as part of their security processes before accepting a foundation model for use. 

    Maliciously modified data – risks and mitigations

    Relevant AI Lifecycle stages: 2) Collect & Process Data; 3) Build & Use Model; 4) Verify & Validate; 5) Deploy & Use; 6) Operate & Monitor

    Maliciously modified data presents a significant threat to the accuracy and integrity of AI systems. Deliberate manipulation of data can result in inaccurate outcomes, poor decisions, and compromised security. Note that there are also risks associated with unintentional data errors and duplications that can affect the security and performance of AI systems. Challenges like adversarial machine learning threats, statistical bias, and inaccurate information can impact the overall security of AI-driven outcomes.

    Risk: Adversarial Machine Learning threats

    Adversarial Machine Learning (AML) threats involve intentional, malicious attempts to deceive, manipulate, or disrupt AI systems. [7],[17],[22] Malicious actors employ data poisoning to corrupt the learning process, compromising the integrity of training datasets and leading to unreliable or malicious model behavior. Additionally, malicious actors may introduce adversarial examples into datasets that, while subtle, can evade correct classification, thereby undermining the model’s performance. Furthermore, sensitive information in training datasets can be indirectly extracted through techniques like model inversion4, posing significant data security risks.

    Mitigation Strategies:

    • Anomaly detection: Incorporate anomaly detection algorithms during data pre-processing to identify and remove malicious or suspicious data points before training. These algorithms can recognize statistically deviant patterns in the data, making it possible to isolate and eliminate poisoned inputs.
    • Data sanitization: Sanitize the training data by applying techniques like data filtering, sampling, and normalization. This helps reduce the impact of outliers, noisy data, and other potentially poisoned inputs, ensuring that models learn from high-quality, representative datasets. Perform sanitization on a regular basis, especially prior to each and every training, fine-tuning, or any other process that adjusts model parameters.
    • Secure training pipelines: Secure data collection, pre-processing, and training pipelines to prevent malicious actors from tampering with datasets or model parameters.
    • Ensemble methods / collaborative learning: Implement collaborative learning frameworks that combine an ensemble of multiple, distinct AI models to reach a consensus on output predictions. This approach can help counteract the impact of data poisoning, since malicious inputs may only affect a subset of the collaborative models, allowing the majority to maintain accuracy and reliability.
    • Data anonymization: Implement anonymization techniques to protect sensitive data attributes, keeping them confidential while allowing AI models to learn patterns and generate accurate predictions.

    Risk: Bad data statements

    Bad data statements5 [7][23], such as missing metadata, can significantly influence AI data security by introducing data integrity issues that can lead to faulty model performance. Error-free metadata provides valuable contextual information about the data, including its structure, purpose, and collection methods. When metadata is missing, it becomes difficult to interpret data accurately and draw meaningful conclusions. This situation can result in incomplete or inaccurate data representation, compromising AI system performance and reliability. If metadata is modified by a malicious actor, then the security of the AI system is also at risk.

    Mitigation strategies:

    • Metadata management: Implement strong data governance practices to help ensure metadata is well-documented, complete, accurate, and secured.
    • Metadata validation: Establish data validation processes to check the completeness and consistency of metadata before data is used for AI training.
    • Data enrichment: Use available resources, such as reference data and trusted third-party data, to supplement missing metadata and improve the overall quality of the training data.

    Risk: Statistical bias6 

    Robust data security and collection practices are key to mitigating statistical bias. Executive Order (EO) 14179 mandates that U.S. government entities “develop AI systems that are free from ideological bias or engineered social agendas.” [25] Note that “an AI system is said to be biased when it exhibits systematically inaccurate behavior.” [26] Statistical bias in AI systems can arise from artifacts present in training data that can lead to artificially slanted or inaccurate outcomes. Sampling biases or biases in data collection can affect the overall outcomes and performance of AI. Left unaddressed, statistical bias can degrade the accuracy and effectiveness of AI systems. 

    Mitigation strategies:

    • Regular training data audits: Regularly audit training data to detect, assess, and address potential issues that can result in systematically inaccurate AI systems.
    • Representative training data: Ensure that training data is representative of the totality of the information relevant to any given topic to reduce the risk of statistical bias. Also ensure that AI data is properly divided into training, development, and evaluation sets without overlap to properly measure statistical bias and other measures of performance.
    • Edge cases: Identify and mitigate edge cases that can cause models to malfunction.
    • Test and correct for statistical bias: Create a repository with instances of observed model output bias. Leverage that information to improve training data audits and with reinforcement learning to “undo” some of the measured bias.

    Risk: Data poisoning via inaccurate information

    One form of data poisoning (sometimes referred to as “disinformation” [27]) involves the intentional insertion of inaccurate or misleading information in AI training datasets, which can negatively impact AI system performance, outcomes, and decision-making processes. 

    Mitigation strategies:

    • Remove inaccurate information from training data: Identify and remove inaccurate or misleading information from AI datasets to the extent feasible.
    • Data provenance and verification: Implement provenance verification mechanisms during data collection to help ensure that only accurate and reliable data is used. This process can include methods such as cross-verification, fact-checking, source analysis, data provenance tracking, and content credentials.
    • Add more training data: Increasing the amount of non-malicious data makes training more robust against poisoned examples—provided that these poisoned examples are small in number. One way to do this is through data augmentation—the creation of artificial training set samples that are small variations of existing samples. The goal is to “outnumber” the poisoned samples so the model “forgets” them. Note that this mitigation can only be applied during training, and therefore does not apply to an already trained model. [28]
    • Data quality control: Perform quality control on data including detecting poisoned samples through integrity checks, statistical deviation, or pattern recognition. Proactively implement data quality controls during the training phase to prevent issues before they arise in production.

    Risk: Data duplications

    Unintended duplicate data elements [7] in training datasets can skew model performance and cause overfitting, reducing the AI model’s ability to generalize across a variety of real-world applications. Duplicates are not always exact; near-duplicates may contain minor differences like formatting, abbreviations, or errors, which makes detecting them more complex. Duplicate data often leads to inaccurate predictions, making the AI system less effective in real-world applications.

    Mitigation strategies:

    • Data deduplication: Implement deduplication techniques (such as fuzzy matching, hashing, clustering, etc.) to carefully identify and handle duplicates and near-duplicates in the data.

    Data drift – risks and mitigations

    Relevant AI Lifecycle stages: 5) Deploy & Use; 6) Operate & Monitor

    Data drift, or distribution shift, refers to changes in the underlying statistical properties of the input data to an operational AI system. Over time, the input data can become significantly different from the data originally used to train the model. [7],[8] Degradation caused by data drift is a natural and expected occurrence, and AI system developers and operators need to regularly update models to maintain accuracy and performance. Data drift ordinarily begins as small, seemingly insignificant degradations in model performance. Left unchecked, the degradation caused by data drift can snowball into substantial reductions in AI system accuracy and integrity that become increasingly difficult to correct. 

    It is crucial to distinguish between data drift and data poisoning attacks designed to affect an AI model. Continuous monitoring of system accuracy and performance provides important indicators based on the nature of the changes observed. If the changes are slow and gradual over time, it is more likely that the model is experiencing data drift. If the changes are abrupt and dramatic in one or more dimensions, it is more likely that an actor is trying to compromise the model. Cyber compromises often aim to manipulate the model’s performance quickly and significantly, leading to abrupt changes in the input data or model outputs.

    AI system operators and developers should employ a wide range of techniques for detecting and mitigating data drift, including data preprocessing, increasing dataset coverage of real-world scenarios, and adopting robust training and adaptation strategies. [30] Packages that automate dataset loading assist AI system developers in creating application-specific detection and mitigation techniques for data drift.

    There are many potential causes of data drift, including: 

    1. A change in the upstream data pipeline not represented in the model training data (e.g., the units of a particular data element change from miles to kilometers)
    2. The introduction of completely new data elements that the model had not previously seen (e.g., a new type of malware not recognized in the ML layer of an anti-virus product)
    3. A change in the context of how inputs and outputs are related (e.g., a change in organizational structure due to a merger or acquisition could lead to new data access patterns that might be misinterpreted as security threats by an AI system)

    The data associated with a given AI model should be regularly checked for any updates to help ensure the model still predicts as expected. [7],[8],[9] The interval for this update and check will depend on the particular AI system and application. For example, in high-stakes applications such as healthcare, early detection and mitigation of data drift are critical prior to patient impact. Thus, continuous monitoring of model performance with additional direct analysis of the input data is important in such applications. [30] 

    Mitigation strategies:

    • Data management: Employ a data management strategy in keeping with the best practices in this CSI to help ensure that it is easy to add and track new data elements for model training and adaptation. This management strategy enables identification of data elements causing drift for appropriate mitigation or action.
    • Data-quality testing: AI system developers should use data-quality assessment tools to assist in selecting and filtering data used for model training or adaptation. Understanding the current dataset and its impact on model behavior is critical to detecting data drift.
    • Input and output monitoring: Monitor the AI system inputs and outputs to verify the model is performing as expected. [9] Regularly update your model using current data. Utilize meaningful statistical methods that measure expected dataset metrics and compare the distribution of the training data to the test data to help determine if data drift is occurring. [7] 

    Data management tools and methods are currently an active area of research. However, data drift can be mitigated by incorporating application-specific data management protocols that include: continuous monitoring, retraining (regularly incorporating the latest data into the models), data cleansing (correcting errors or inconsistencies in the data), and using ensemble models (combining predictions of multiple models). Incorporation of a data management framework into the design of AI systems from the beginning is essential for improving the overall integrity and security posture. [31]

    Conclusion

    Data security is of paramount importance when developing and operating AI systems. As organizations in various sectors rely more and more on AI-driven outcomes, data security becomes crucial for maintaining accuracy, reliability, and integrity. The guidance provided in this CSI outlines a robust approach to securing AI data and addressing the risks associated with the data supply chain, malicious data, and data drift.

    Data security is an ever-evolving field, and continuous vigilance and adaptation are key to staying ahead of emerging threats and vulnerabilities. The best practices presented here encourage the highest standards of data security in AI while helping ensure the accuracy and integrity of AI-driven outcomes. By adopting these best practices and risk mitigation strategies, organizations can fortify their AI systems against potential threats and safeguard sensitive, proprietary, and mission critical data used in the development and operation of their AI systems. 

    References

    1 In this document, Artificial Intelligence (AI) has the meaning set forth in 15 U.S.C. 9401(3): 
    “… a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems use machine- and human-based inputs to:
      (A) Perceive real and virtual environments;
      (B) Take these perceptions and turn them into models through analysis in an automated manner; and
      (C) Use model inference to formulate options for information or action.”

    2 Data integrity is defined by the IC Data Management Lexicon [1] as “The degree to which data can be trusted due to its provenance, pedigree, lineage and conformance with all business rules regarding its relationship with other data. In the context of data movement, this is the degree to which data has verifiably not been changed unexpectedly by a person or NPE.”

    3 The term data consumers is defined as technical personnel (e.g. data scientists, engineers) who make use of data that they themselves did not produce or annotate to build and/or operate AI systems. 

    4 Model inversion refers to the process by which an attacker analyzes the output patterns of an AI system to reverse-engineer and uncover details about the training dataset, such as individual data points or patterns. This process can potentially expose confidential or proprietary information from the data that was used to train the AI models.

    5 “A data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.” [23] 

    6 “In technical systems, bias is most commonly understood and treated as a statistical phenomenon. Bias is an effect that deprives a statistical result of representativeness by systematically distorting it, as distinct from random error, which may distort on any one occasion but balances out on the average.” [26],[32] 

    Works cited

    [1] Office of the Director of National Intelligence. The Intelligence Community Data Management Lexicon. 2024. https://dni.gov/files/ODNI/documents/IC_Data_Management_Lexicon.pdf   
    [2] National Security Agency et al. Deploying AI Systems Securely: Best Practices for Deploying Secure and Resilient AI Systems. 2024. https://media.defense.gov/2024/Apr/15/2003439257/-1/-1/0/CSI-DEPLOYING-AI-SYSTEMS-SECURELY.PDF  
    [3] National Institute of Standards and Technology (NIST). NIST AI 100-1: Artificial Intelligence Risk Management Framework (AI RMF 1.0). 2023. https://doi.org/10.6028/NIST.AI.100-1  
    [4] NIST. NIST Special Publication 800-37 Rev. 2: Guide for Applying the Risk Management Framework to Federal Information Systems. 2018. https://doi.org/10.6028/NIST.SP.800-37r2  
    [5] NIST. Federal Information Processing Standards Publication (FIPS) 204: Module-Lattice-Based Digital Signature Standard. 2024. https://doi.org/10.6028/NIST.FIPS.204  
    [6] NIST. FIPS 205: Stateless Hash-Based Digital Signature Standard. 2024. https://doi.org/10.6028/NIST.FIPS.205  
    [7] Bommasani, R. et al. On the Opportunities and Risks of Foundation Models. arXiv:2108.07258v3. 2022. https://arxiv.org/abs/2108.07258v3  
    [8] Securing Artificial Intelligence (SAI); Data Supply Chain Security. ESTI GR SAI 002 V1.1.1. 2021. https://etsi.org/deliver/etsi_gr/SAI/001_099/002/01.01.01_60/gr_SAI002v010101p.pdf  
    [9] National Cyber Security Centre et al. Guidelines for Secure AI System Development. 2023. https://www.ncsc.gov.uk/files/Guidelines-for-secure-AI-system-development.pdf  
    [10] NIST. NIST Special Publication 800-207: Zero Trust Architecture. 2020. https://doi.org/10.6028/NIST.SP.800-207  
    [11] NIST. NIST IR 8496 ipd: Data Classification Concepts and Considerations for Improving Data Protection. 2023. https://doi.org/10.6028/NIST.IR.8496.ipd  
    [12] Cybersecurity and Infrastructure Security Agency (CISA), NSA, and NIST. Quantum-Readiness: Migration to Post-Quantum Cryptography. 2023. https://www.cisa.gov/resources-tools/resources/quantum-readiness-migration-post-quantum-cryptography 
    [13] NIST. FIPS 203: Module-Lattice-Based Key-Encapsulation Mechanism Standard. 2024. https://doi.org/10.6028/NIST.FIPS.203  
    [14] NIST. NIST SP 800-52 Rev. 2: Guidelines for the Selection, Configuration, and Use of Transport Layer Security (TLS) Implementations. 2019. https://doi.org/10.6028/NIST.SP.800-52r2  
    [15] NIST. FIPS 140-3, Security Requirements for Cryptographic Modules. 2019. https://doi.org/10.6028/NIST.FIPS.140-3    
    [16] NIST. FIPS 140-2, Security Requirements for Cryptographic Modules. 2001. https://doi.org/10.6028/NIST.FIPS.140-2  
    [17] NIST. NIST AI 100-2e2023: Trustworthy and Responsible AI, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations. 2024. https://doi.org/10.6028/NIST.AI.100-2e2023  
    [18] Adak, M. F., Kose, Z. N., & Akpinar, M. Dynamic Data Masking by Two-Step Encryption. In 2023 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-5). IEEE. 2023 https://doi.org/10.1109/ASYU58738.2023.10296545    
    [19] Kairouz, P. et al. Advances and Open Problems in Federated Learning. Foundations and Trends in Machine Learning 14 (1-2): 1-210. arXiv:1912.04977. 2021. https://arxiv.org/abs/1912.04977  
    [20] NIST. NIST SP 800-88 Rev. 1: Guidelines for Media Sanitization. 2014. https://doi.org/10.6028/NIST.SP.800-88r1  
    [21] NIST. NIST Special Publication 800-3 Rev. 2: Risk Management Framework for Information Systems and Organizations: A System Life Cycle Approach for Security and Privacy. 2018. https://doi.org/10.6028/NIST.SP.800-37r2  
    [22] U.S. Department of Homeland Security. Preparedness Series June 2023: Risks and Mitigation Strategies for Adversarial Artificial Intelligence Threats: A DHS S&T Study. 2023. https://www.dhs.gov/sites/default/files/2023-12/23_1222_st_risks_mitigation_strategies.pdf  
    [23] Bender, E. M., & Friedman, B. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics (TACL) 6, 587–604. 2018. https://doi.org/10.1162/tacl_a_00041  
    [24] NSA et al. Content Credentials: Strengthening Multimedia Integrity in the Generative AI Era. 2025. https://media.defense.gov/2025/Jan/29/2003634788/-1/-1/0/CSI-CONTENT-CREDENTIALS.PDF  
    [25] Executive Order (EO) 14179: “Removing Barriers to American Leadership in Artificial Intelligence” https://www.federalregister.gov/executive-order/14179   
    [26] NIST. NIST Special Publication 1270: Framework for Identifying and Managing Bias in Artificial Intelligence. 2023. https://doi.org/10.6028/NIST.SP.1270  
    [27] NIST. NIST AI 600-1: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. 2023. https://doi.org/10.6028/NIST.AI.600-1  
    [28] Open Web Application Security Project (OWASP). AI Exchange. #Moretraindata. https://owaspai.org/goto/moretraindata/  
    [29] Carlini, N. et al. Poisoning Web-Scale Training Datasets is Practical. arXiv:2302.10149. 2023. https://arxiv.org/abs/2302.10149  
    [30] Kore, A., Abbasi Bavil, E., Subasri, V., Abdalla, M., Fine, B., Dolatabadi, E., & Abdalla, M. Empirical Data Drift Detection Experiments on Real-World Medical Image Data. Nature Communications 15, 1887. 2024. https://doi.org/10.1038/s41467-024-46142-w  
    [31] NIST. NIST Special Publication 800-208: Recommendation for Stateful Hash-Based Signature Schemes. 2020. https://doi.org/10.6028/NIST.SP.800-208  
    [32] The Organisation for Economic Cooperation and Development (OECD). Glossary of statistical terms. 2008. https://doi.org/10.1787/9789264055087-en  
    [33] NIST. NIST SP 800-53 Rev. 5: Security and Privacy Controls for Information Systems and Organizations. 2020. https://doi.org/10.6028/NIST.SP.800-53r5 
    [34] OWASP. AI Exchange. How to select relevant threats and controls? risk analysis. https://owaspai.org/goto/riskanalysis/  

    Disclaimer of Endorsement

    The information and opinions contained in this document are provided “as is” and without any warranties or guarantees. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the United States Government, and this guidance shall not be used for advertising or product endorsement purposes.

    Purpose

    This document was developed in furtherance of the authoring organizations’ cybersecurity missions, including their responsibilities to identify and disseminate threats, and to develop and issue cybersecurity specifications and mitigations. This information may be shared broadly to reach all appropriate stakeholders. 

    Notice of Generative AI Use

    Generative AI technology was carefully and responsibly used in the development of this document. The authors maintain ultimate responsibility for the accuracy of the information provided herein.

    Contact 

    U.S. Organizations

    National Security Agency

    Australian organizations

    • Visit cyber.gov.au/report or call 1300 292 371 (1300 CYBER1) to report cybersecurity incidents and vulnerabilities.

    New Zealand organizations

    MIL OSI USA News

  • MIL-OSI USA: Moran Applauds House Passage of ‘One, Big, Beautiful Bill’ to Reignite the American Dream

    Source: Congressman Nathaniel Moran (R-TX-01)

    Today, Congressman Nathaniel Moran (R-TX-01) released the following statement after the House passed the “One, Big, Beautiful Bill,” sending it to the Senate for consideration:

     

    Washington, D.C.—Today, Congressman Nathaniel Moran (R-TX-01) released the following statement after the House passed the “One, Big, Beautiful Bill,” sending it to the Senate for consideration:

    “With today’s passage of the One, Big, Beautiful Bill, House Republicans delivered on the promises we made to the American people. This legislation puts working families, small businesses, and rural communities back at the center of our economic future—right where they belong.

    “In Texas’ First Congressional District, where the median income is just $62,000, a family of four was on track to see their taxes increase by over $1,100—a staggering 22% hike—had we failed to act. That’s six weeks’ worth of groceries. That’s money that could fix a truck, invest in a small business, or be saved for a child’s future. By passing this bill, we’ve protected those hard-earned dollars. But more than that, we’ve advanced liberty by empowering families, workers, and small businesses to thrive without the government taking more of what they earn. This bill expands opportunity, restores dignity in work, and strengthens the American Dream. That’s worth fighting for.”


    Watch Congressman Moran’s Full Remarks 
    HERE

    Background on the “One, Big, Beautiful Bill”: 

    For Small Businesses:

    • Makes permanent the 199A small business deduction and expands to 23%, supporting over 1 million new jobs and generating $750 billion in economic growth

    • Reinstates immediate expensing for R&D

    • Revitalizes American manufacturing by renewing 100% immediate expensing for new factories, equipment, and facility improvements

    • Doubles section 179 Small Business Expensing to $2.5 million, allowing small businesses to invest in their employees

    • Reduces administrative burdens by repealing the Democrats’ $600 1099-K gig worker rule, and re-setting it to $2,000 threshold

    For Families:

    • Expands tax relief for families and seniors—including no tax on tips, relief on car loan interest, tax relief for those working overtime, and additional tax relief for seniors

    • Expands the enhanced standard deduction and increases the Child Tax Credit for over 40 millions families

    • Empowers working families through permanent paid leave tax credits, expanded child care access, and new savings accounts for every child at birth

    • Increases access to the Adoption Tax Credit for those families looking to change the lives of our little ones through the gift of adoption

    For Rural America:

    • Protects family farms and rural small businesses by making the doubled Death Tax exemption permanent and increasing it

    • Revives and expands Opportunity Zones to bring $100 billion in investment to rural and distressed communities

    • Unleashes rural growth with 100% expensing for new factories, agricultural improvements, and equipment—empowering producers to expand and invest 

    For the Broader Economy:

    What’s at Stake:

    • Without this bill, a family of four earning the national median income ($80,610) will face a $1,695 tax hike starting in 2026—equal to 9 weeks of groceries

    • In Texas’ First Congressional District, families earning the median income of $62,182 will see a $1,142 increase—a staggering 22% spike in their tax bill

    ###

    MIL OSI USA News

  • MIL-OSI USA: LaMalfa Applauds House Passage of Budget Reconciliation Package

    Source: United States House of Representatives – Congressman Doug LaMalfa 1st District of California

    Washington, D.C.—Congressman Doug LaMalfa (R-Richvale) released the following statement after the House passed the budget reconciliation package.

    “This bill is the course correction we desperately needed. For years, taxpayers have been footing the bill for wasteful programs, unchecked spending, and handouts to people gaming the system,” said Rep. LaMalfa. “It delivers a significant tax cut, providing relief for working families and much needed reforms to government spending. It starts the process of eliminating fraud in programs like Medicaid and SNAP. It also includes major wins for folks in the West, allocating $2 billion to expand water storage, and much needed funding for the Secure Rural Schools program. It’s a big win across the board, and I’m happy to see the House pass this crucial piece of legislation.”

    Background:

    • Tax Relief for Working Americans: Delivers significant tax cuts, cutting tax bills by about 15% for Americans earning $30,000–$80,000. No tax on tips or overtime.
    • Medicaid and SNAP Reform: Ends benefits for 1.4 million illegal immigrants and restores work requirements for able-bodied adults receiving assistance.
    • Support for Farmers: Strengthens crop insurance and conservation tools without adding red tape.
    • Water Storage Expansion: Includes $2 billion to upgrade and expand Bureau of Reclamation surface water storage, helping the West store more water in wet years.
    • Secure Rural Schools: Provides a 3-year reauthorization of SRS to support education in rural areas with large amounts of federal land.
    • Energy and Resource Development: Repeals Green New Deal-style handouts and boosts American oil, gas, and mineral production.
    • Border Security and Immigration Enforcement: Fully funds Trump’s border wall, ramps up deportations, and hires thousands of new ICE and Border Patrol agents.

    Congressman Doug LaMalfa is Chairman of the Congressional Western Caucus and a lifelong farmer representing California’s First Congressional District, including Butte, Colusa, Glenn, Lassen, Modoc, Shasta, Siskiyou, Sutter, Tehama and Yuba Counties.

    ###

    MIL OSI USA News

  • MIL-OSI: Intchains Group Limited Reports First Quarter 2025 Unaudited Financial Results

    Source: GlobeNewswire (MIL-OSI)

    Total revenues of US$18.2 million exceeds guidance, up 445.5% YoY

    Total ETH-based cryptocurrency units were approximately 7,023, up 23.2% QoQ

    Income from operations reach US$5.1 million, achieving turnaround from prior-year period

    SINGAPORE, May 22, 2025 (GLOBE NEWSWIRE) — Intchains Group Limited (Nasdaq: ICG) (“we,” or the “Company”), a company that engages in the provision of altcoin mining products, the strategic acquisition and holding of Ethereum-based cryptocurrencies, and the active development of innovative Web3 applications, today announced its unaudited financial results for the first quarter ended March 31, 2025.

    First Quarter 2025 Operating and Financial Highlights

    • Sales Volume of Altcoin Mining Products Measured by Number of Embedded ASIC Chips: Since we offer a wide range of altcoin mining products, with each unit incorporating anywhere from tens to hundreds of ASIC chips, it is more meaningful to measure the sales of our altcoin mining products by the number of embedded ASIC chips. Our sales volume of ASIC chips for Q1 2025 was 709,857 units, compared to 494,235 units for the same period last year, representing an increase of 43.6%.
    • Revenue: Our revenue for Q1 2025 reached RMB132.4 million (US$18.2 million), reflecting a increase of 445.5% from RMB24.3 million for the same period of 2024.
    • Income/(Loss) from Operations: We recorded income from operations of RMB36.9 million (US$5.1 million) for Q1 2025, compared to a loss from operations of RMB34.6 million for the same period of 2024.
    • Net Loss: Our net loss for Q1 2025 was RMB34.0 million (US$4.7 million), reflecting an increase of 129.8% from RMB14.8 million for the same period in 2024.
    • Non-GAAP Adjusted Net Loss: Non-GAAP adjusted net loss in the first quarter of 2025 was RMB32.0 million (US$4.4 million), reflecting an increase of 139.6% from RMB13.3 million for the same period in 2024. Non-GAAP adjusted net loss excludes share-based compensation expenses. For further information, please refer to “Use of Non-GAAP Financial Measures” in this press release.
    • Cryptocurrencies: As of March 31, 2024, the fair value of our cryptocurrency assets other than stablecoins such as USDT and USDC was RMB101.6 million (US$14.0 million), primarily comprised of approximately 7,023 ETH-based cryptocurrencies, valued at RMB93.7 million (US$13.1 million).

    Intchains Group Achieves Milestones in Innovative Solutions and Cryptocurrency Strategy

    Mr. Qiang Ding, Chairman of the Board of Directors and Chief Executive Officer, commented, “In the first quarter of 2025, the cryptocurrency market encountered considerable headwinds. Nevertheless, the Company demonstrated agility and foresight by promptly launching the Aleo series mining machines in response to shifting market dynamics. These altcoin mining machines delivered substantial profitability for miners amid challenging macro market conditions while driving sustainable corporate growth –further validating our expertise in altcoin mining machine innovations and our competitive edge through differentiated market positioning.

    In addition, the Company introduced Goldshell Byte, an innovative dual-mining machine. This milestone reflects the Company’s unique capability to design and manufacture advanced mining machines spanning multiple altcoin protocols. The modular design—featuring a standard miner with pluggable mining cards—offers strategic flexibility for miners and encourages wider participation by retail users. Its compact, home-friendly form factor further promotes widespread participation in the decentralized network.

    During the quarter, small- and mid-cap cryptocurrencies, including Ethereum, experienced downward pressure. Despite this, the Company remained committed to its long-term dollar-cost averaging strategy. As of March 31, 2025, the Company held approximately 7,023 ETH, representing a 23.2% increase quarter-over-quarter.

    In the second quarter of 2025, Ethereum completed its Pectra upgrade, and the Ethereum Foundation reaffirmed its long-term vision with the appointment of a new board of directors. The Company views these developments as positive signals and continues to believe in the enduring value of blockchain technology. As a long-term accumulator of Ethereum, the Company will continue to build its position in alignment with its strategic outlook on decentralized applications.”

    First Quarter 2025 Financial Results

    Revenue

    Revenue was RMB132.4 million (US$18.2 million) for the first quarter of 2025, representing an increase of 445.5% from RMB24.3 million for the same period in 2024. The substantial growth was primarily driven by strong market demand for our newly-launched Aleo series mining machines, which accounted for 74.8% of the total revenue for the first quarter of 2025.

    Cost of Revenue

    Cost of revenue was RMB57.0 million (US$7.9 million) for the first quarter of 2025, representing an increase of 273.8% from RMB15.3 million for the same period of 2024. The percentage increase in cost of revenue was lower than the percentage increase in our revenue, which was primarily due to the higher gross margins for the Aleo series mining machines sold in the first quarter of 2025.

    Operating Expenses

    Total operating expenses were RMB38.4 million (US$5.3 million) for the first quarter of 2025, representing a decrease of 11.8% from RMB43.6 million for the same period of 2024. The decrease was primarily due to a decrease in research and development expenses, partially offset by an increase of general and administrative expenses.

    • Research and development expenses decreased by 27.9% to RMB26.4 million (US$3.6 million) for the first quarter of 2025 from RMB36.5 million for the same period of 2024. The decrease was primarily due to lower expenses related to preliminary research costs conducted for new projects.
    • Sales and marketing expenses increased by 37.8% to RMB2.2 million (US$0.3 million) for the first quarter of 2025 from RMB1.6 million for the same period of 2024, mainly driven by increased personnel-related expenses.
    • General and administrative expenses increased by 81.8% to RMB9.8 million (US$1.4 million) for the first quarter of 2025 from RMB5.4 million for the same period of 2024, mainly driven by increased professional fees, as well as the personnel-related expenses.

    Interest Income

    Interest income decreased by 24.0% to RMB3.2 million (US$0.4 million) for the first quarter of 2025 from RMB4.2 million for the same period of 2024, mainly due to a lower cash level resulting from our strategy of allocating part of our operating cash flow to acquire ETH-based cryptocurrencies.

    Change in fair value of cryptocurrencies

    Change in fair value of cryptocurrencies was RMB70.8 million (US$9.8 million) loss for the first quarter of 2025, compared to RMB5.4 million gain for the same period of 2024. The loss was primarily due to an approximately 46.0% decline in the price of ETH, while we simultaneously increased our holdings of ETH-based cryptocurrency as part of our ongoing ETH accumulation strategy.

    Other Income, Net

    Other income, net remained steady at RMB0.1 million and RMB0.2 million (US$0.03 million), respectively, for the first quarter of 2024 and 2025.

    Net Loss

    As a result of the foregoing, our net loss increased by 129.8% to RMB34.0 million (US$4.7 million) for the first quarter of 2025 from RMB14.8 million for the same period of 2024.

    Non-GAAP Adjusted Net Loss

    Non-GAAP adjusted net loss increased by 139.6% to RMB32.0 million (US$4.4 million) for the first quarter of 2025 from RMB13.3 million for the same period of 2024.

    Basic and Diluted Net Loss Per Ordinary Share

    Basic and diluted net loss per ordinary share both increased by 133.3% to RMB0.28 (US$0.04) for the first quarter of 2025 from RMB0.12 for the same period of 2024.

    Non-GAAP Basic and Diluted Net Loss Per Ordinary Share

    Non-GAAP adjusted basic and diluted net loss per ordinary share increased by 145.5% to RMB0.27 (US$0.04) for the first quarter of 2025 from RMB0.11 for the same period of 2024. Each ADS represents two of the Company’s Class A ordinary shares.

    Recent Development

    Aleo Mining: In the first quarter of 2025, we led the market with the launch of our Aleo series mining machines, which were well-received by the crypto mining communities globally despite sustained macro market pressures. By the end of May 2025, we had released five key models of the Aleo series, which have demonstrated strong competitiveness in the PoW sector in terms of daily profitability.

    Goldshell Byte: On March 26, 2025, we officially launched Goldshell Byte, our latest flagship product, and an innovative dual-mining machine. Designed to allow miners to dynamically respond to market changes, Goldshell Byte combines standardized hardware with modular pluggable cards, drawing upon the our deep and extensive experience across multiple altcoin ecosystems. This innovation is expected to further strengthen our market position in the altcoin mining space.

    Conference Call Information

    The Company’s management team will host an earnings conference call to discuss its financial results at 8:00 PM U.S. Eastern Time on May 22, 2025 (8:00 AM Beijing Time on May 23, 2025). Details for the conference call are as follows:

    Event Title: Intchains Group Limited First Quarter 2025 Earnings Conference Call

    Date: May 22, 2025

    Time: 8:00 PM U.S. Eastern Time

    Registration Link: https://register-conf.media-server.com/register/BI0dda68e5b19a4a7daade5ed1cf188ed8

    All participants must use the link provided above to complete the online registration process in advance of the conference call. Upon registering, each participant will receive a set of dial-in numbers and a personal access PIN, which will be used to join the conference call.

    Additionally, a live and archived webcast of the conference call will also be available at the Company’s website at https://ir.intchains.com/.

    About Intchains Group Limited

    Intchains Group Limited is a company that engages in the provision of altcoin mining products, the strategic acquisition and holding of Ethereum-based cryptocurrencies, and the active development of innovative Web3 applications. For more information, please visit the Company’s website at: https://intchains.com/.

    Exchange Rate Information

    The unaudited United States dollar (“US$”) amounts disclosed in the accompanying financial statements are presented solely for the convenience of the readers. Translations of amounts from RMB into US$ for the convenience of the reader were calculated at the noon buying rate of US$1.00=RMB7.2567 on the last trading day of the first quarter of 2025 (March 31, 2025). No representation is made that the RMB amounts could have been, or could be, converted into US$ at such rate.

    Forward-Looking Statements

    Certain statements in this announcement are forward-looking statements. These forward-looking statements involve known and unknown risks and uncertainties and are based on the Company’s current expectations and projections about future events that the Company believes may affect its financial condition, results of operations, business strategy and financial needs. Forward-looking statements include, but are not limited to, statements about: (i) our goals and strategies; (ii) our future business development, formed condition and results of operations; (iii) expected changes in our revenue, costs or expenditures; (iv) growth of and competition trends in our industry; (v) our expectations regarding demand for, and market acceptance of, our products; (vi) general economic and business conditions in the markets in which we operate; (vii) relevant government policies and regulations relating to our business and industry; (viii) fluctuations in the market price of ETH-based cryptocurrencies; gains or losses from the sale of ETH-based cryptocurrencies; changes in accounting treatment for the Company’s ETH-based cryptocurrencies holdings; a decrease in liquidity in the markets in which ETH-based cryptocurrencies are traded; security breaches, cyberattacks, unauthorized access, loss of private keys, fraud, or other events leading to the loss of the Company’s ETH-based cryptocurrencies; impacts to the price and rate of adoption of ETH-based cryptocurrencies associated with financial difficulties and bankruptcies of various participants in the industry; and (viii) assumptions underlying or related to any of the foregoing. Investors can identify these forward-looking statements by words or phrases such as “may,” “could,” “will,” “should,” “would,” “expect,” “plan,” “intend,” “anticipate,” “believe,” “estimate,” “predict,” “potential,” “project” or “continue” or the negative of these terms or other comparable terminology. The Company undertakes no obligation to update or revise publicly any forward-looking statements to reflect subsequent occurring events or circumstances, or changes in its expectations, except as may be required by law. Although the Company believes that the expectations expressed in these forward-looking statements are reasonable, it cannot assure you that such expectations will turn out to be correct, and the Company cautions investors that actual results may differ materially from the anticipated results and encourages investors to review other factors that may affect its future results in the Company’s registration statement and other filings with the SEC.

    Use of Non-GAAP Financial Measures

    In evaluating Company’s business, the Company uses non-GAAP measures, such as adjusted income (loss) from operations and adjusted net income (loss), as supplemental measures to review and assess its operating performance. The Company defines adjusted income (loss) from operations as income (loss) from operations excluding share-based compensation expenses, and adjusted net income (loss) as net income (loss) excluding share-based compensation expenses. The Company believes that the non-GAAP financial measures provide useful information about the Company’s results of operations, enhance the overall understanding of the Company’s past performance and future prospects and allow for greater visibility with respect to key metrics used by the Company’s management in its financial and operational decision-making.

    The non-GAAP financial measures are not defined under U.S. GAAP and are not presented in accordance with U.S. GAAP. The non-GAAP financial measures have limitations as analytical tools and investors should not consider them in isolation, or as a substitute for net income, cash flows provided by operating activities or other consolidated statements of operations and cash flows data prepared in accordance with U.S. GAAP. One of the key limitations of using adjusted net income is that it does not reflect all of the items of income and expense that affect the Company’s operations. Share based compensation expenses have been and may continue to be incurred in Company’s business and are not reflected in the presentation of adjusted net income. Further, the non-GAAP financial measures may differ from the non-GAAP information used by other companies, including peer companies, and therefore their comparability may be limited. The Company mitigates these limitations by reconciling the non-GAAP financial measures to the most comparable U.S. GAAP performance measures, all of which should be considered when evaluating the Company’s performance.

    For investor and media inquiries, please contact:

    Intchains Group Limited
    Investor relations
    Email: ir@intchains.com

    Redhill
    Belinda Chan
    Tel: +852-9379-3045
    Email: belinda.chan@creativegp.com

    INTCHAINS GROUP LIMITED
    UNAUDITED CONDENSED CONSOLIDATED BALANCE SHEETS
    (All amounts in thousands, except share and per share data, or as otherwise noted)

      As of December 31,   As of March 31
      2024    2025
      RMB   RMB US$
    ASSETS        
    Current Assets:        
    Cash and cash equivalents 322,252     243,316   33,530
    USDC 1,690     3,458   476
    Cryptocurrency, current 30,079     11,674   1,609
    Inventories, net 98,614     92,494   12,746
    Prepayments and other current assets, net 69,703     67,857   9,351
    Short-term investments 198,562     300,530   41,414
    Total current assets 720,900     719,329   99,126
    Non-current Assets:        
    Cryptocurrencies, non-current 148,790     101,566   13,996
    Long-term investments 20,569     21,913   3,020
    Property, equipment, and software, net 157,065     155,934   21,489
    Intangible assets, net 3,552     3,424   472
    Right-of-use assets 272      
    Deferred tax assets 28,942     26,173   3,607
    Other non-current assets 9,419     9,712   1,338
    Total non-current assets 368,609     318,722   43,922
    Total assets 1,089,509     1,038,051   143,048
    LIABILITIES, AND SHAREHOLDERS’ EQUITY        
    Current Liabilities:        
    Accounts payable 14,847     5,191   715
    Contract liabilities 37,447     28,866   3,979
    Income tax payable 2,023     1,241   171
    Lease liabilities 272      
    Provision for warranty 161     241   33
    Accrued liabilities and other current liabilities 21,692     17,367   2,393
    Total current liabilities 76,442     52,906   7,291
    Total liabilities 76,442     52,906   7,291
    Shareholders’ Equity:        
    Ordinary shares (US$0.000001 par value; 50,000,000,000 shares authorized, 120,081,456 and 120,803,478 shares issued, 120,020,962 and 120,742,984 shares outstanding as of December 31, 2024 and March 31, 2025, respectively) 1     1  
    Subscriptions receivable from shareholders (1 )   (1 )
    Additional paid-in capital 195,236     201,629   27,785
    Statutory reserves 51,762     51,912   7,154
    Accumulated other comprehensive income 3,777     3,459   477
    Retained earnings 762,292     728,145   100,341
    Total shareholders’ equity 1,013,067     985,145   135,757
    Total liabilities and shareholders’ equity 1,089,509     1,038,051   143,048
    INTCHAINS GROUP LIMITED
    UNAUDITED CONDENSED CONSOLIDATED STATEMENTS OF OPERATIONS AND COMPREHENSIVE LOSS
    (All amounts in thousands, except share and per share data, or as otherwise noted)
      For the Three Months ended March 31,  
      2024    2025  
      RMB   RMB US$  
    Products revenue 24,271     132,391   18,244  
    Cost of revenue (15,262 )   (57,045 ) (7,861 )
    Gross profit 9,009     75,346   10,383  
    Operating expenses:        
    Research and development expenses (36,540 )   (26,354 ) (3,632 )
    Sales and marketing expenses (1,623 )   (2,237 ) (308 )
    General and administrative expenses (5,410 )   (9,838 ) (1,356 )
    Total operating expenses (43,573 )   (38,429 ) (5,296 )
    Income/(Loss) from operations (34,564 )   36,917   5,087  
    Interest income 4,150     3,154   435  
    Foreign exchange loss, net (254 )   (179 ) (25 )
    Change in fair value of cryptocurrencies 5,442     (70,814 ) (9,758 )
    Other income, net 139     193   27  
    Loss before income tax expenses (25,087 )   (30,729 ) (4,234 )
    Income tax (expense)/benefit 10,292     (3,268 ) (450 )
    Net loss (14,795 )   (33,997 ) (4,684 )
    Foreign currency translation adjustment, net of nil tax 108     (318 ) (44 )
    Total comprehensive loss (14,687 )   (34,315 ) (4,728 )
             
    Weighted average number of shares used in per share calculation        
    — Basic 119,888,044     120,053,052   120,053,052  
    — Diluted 119,888,044     120,053,052   120,053,052  
    Net loss per share        
    — Basic (0.12 )   (0.28 ) (0.04 )
    — Diluted (0.12 )   (0.28 ) (0.04 )
    INTCHAINS GROUP LIMITED
    RECONCILIATIONS OF GAAP AND NON-GAAP RESULTS
    (All amounts in thousands, except per share data)
      For the Three Months ended March 31,
      2024   2025
      RMB   RMB US$
    Income/(Loss) from operations (34,564 )   36,917   5,087  
    Add:        
    Share-based compensation expense 1,452     2,022   279  
    Non-GAAP adjusted operating income/(loss) (33,112 )   38,939   5,366  
    Net loss (14,795 )   (33,997 ) (4,684 )
    Add:        
    Share-based compensation expense 1,452     2,022   279  
    Non-GAAP adjusted net loss (13,343 )   (31,975 ) (4,405 )
             
    Non-GAAP adjusted net loss per share        
    — Basic (0.11 )   (0.27 ) (0.04 )
    — Diluted (0.11 )   (0.27 ) (0.04 )
    INTCHAINS GROUP LIMITED
    UNAUDITED CRYPTOCURRENCY-ADDITIONAL INFORMATION
     
    As of Quarter Ended Cryptocurrency Approximate
    Number of
    Cryptocurrency
    Held at End of
    Quarter
    Original Cost
    Basis
    Approximate
    Average Cost
    Price Per Unit
    of
    Cryptocurrency
    Lowest Market
    Price Per Unit of
    Cryptocurrency
    During Quarter
    (a)
    Market Value of
    Cryptocurrency
    Held at End of
    Quarter Using
    Lowest Market
    Price (b)
    Highest Market
    Price Per Unit of
    Cryptocurrency
    During Quarter
    (c)
    Market Value of
    Cryptocurrency
    Held at End of
    Quarter Using
    Highest Market
    Price (d)
    Market Price
    Per Unit of
    Cryptocurrency at End of Quarter
    (e)
    Market Value of
    Cryptocurrency
    Held at End of
    Quarter Using
    Ending Market
    Price (f)
        Unit USD USD USD USD USD USD USD USD
    March 31, 2025 ETH 6,347 18,031,664 2,841 1,754 11,132,638 3,746 23,775,862 1,842 11,691,174
    ETH-Coinbase Staked 676 1,954,713 2,892 1,914 1,293,864 4,065 2,747,940 2,017 1,363,492
    Bitcoin 12.66 946,882 74,793 76,555 969,186 109,358 1,384,472 83,416 1,056,047
    USDT&USDC 2,108,065 2,111,681 1 1 2,091,378 1 2,124,947 1 2,107,951
    Others Multiple * 84,283 Multiple * Multiple * 33,817 Multiple * 94,121 Multiple * 37,553
      Total   23,129,223     15,520,883   30,127,342   16,256,217
                         
    December 31, 2024 ETH 5,075 15,102,524 2,976 2,309 11,718,175 4,109 20,853,175 3,414 17,326,050
    ETH-Coinbase Staked 627 1,800,713 2,872 2,487 1,559,349 4,450 2,790,150 3,701 2,320,527
    Bitcoin 10.29 720,567 70,026 58,864 605,711 108,389 1,115,323 95,285 980,483
    USDT&USDC 4,425,484 4,428,159 1 1 4,384,335 1 4,469,357 1 4,419,574
    Others Multiple * 78,298 Multiple * Multiple * 30,694 Multiple * 101,589 Multiple * 69,389
      Total   22,130,261     18,298,264   29,329,594   25,116,023
                         
    September 30, 2024 ETH 3,522 10,115,116 2,872 2,116 7,452,552 3,563 12,548,886 2,596 9,143,112
    ETH-Coinbase Staked 627 1,800,713 2,872 2,290 1,435,830 3,926 2,461,602 2,807 1,759,989
    Bitcoin 8.47 549,364 64,860 49,050 415,454 70,000 592,900 63,552 538,285
    USDT&USDC 9,847,687 9,849,266 1 1 9,814,682 1 9,857,395 1 9,845,929
    Others Multiple * 105,405 Multiple * Multiple * 36,415 Multiple * 72,441 Multiple * 53,661
      Total   22,419,864     19,154,933   25,533,224   21,340,976
                         
    June 30, 2024 ETH 1,937 6,179,744 3,190 2,814 5,450,718 3,974 7,697,638 3,394 6,574,178
    ETH-Coinbase Staked 480 1,301,108 2,711 2,954 1,417,920 4,243 2,036,640 3,645 1,749,600
    Bitcoin 3.95 265,883 67,312 56,500 223,175 72,777 287,469 61,613 243,371
    USDT&USDC 10,422,648 10,423,276 1 1 10,386,315 1 10,458,980 1 10,404,063
    Others Multiple * 107,484 Multiple * Multiple * 54,226 Multiple * 122,435 Multiple * 64,202
    Total   18,277,495     17,532,354   20,603,162   19,035,414
                         
    March 31,2024 ETH 346 999,180 2,888 2,100 726,600 4,094 1,416,524 3,618 1,251,828
    ETH-Coinbase Staked 479 1,297,687 2,709 2,236 1,071,044 4,341 2,079,339 3,842 1,840,318
    Bitcoin 0.67 44,995 67,157 38,501 25,796 73,836 49,470 70,407 47,173
    USDT&USDC 99,583 99,583 1 1 99,583 1 99,583 1 99,583
    Others Multiple * 81,571 Multiple * Multiple * 67,814 Multiple * 124,481 Multiple * 91,346
    Total   2,523,016     1,990,837   3,769,397   3,330,248

    * The ‘Others’ category encompasses various cryptocurrencies that are not reported individually due to their lower significance. This category is labeled as ‘Multiple’ to indicate the presence of diverse prices associated with different type of cryptocurrency. Due to their immaterial nature, detailed price listings are not provided.
    (a) The “Lowest Market Price Per Unit of Cryptocurrency During Quarter” represents the lowest market price for a single unit of cryptocurrency reported on the Coinbase exchange during the respective quarter, without regard to when we obtained any of the cryptocurrency.
    (b) The “Market Value of Cryptocurrency Held at End of Quarter Using Lowest Market Price” represents a mathematical calculation consisting of the lowest market price for a single unit of cryptocurrency reported on the Coinbase exchange during the respective quarter multiplied by the number of cryptocurrency we held at the end of the applicable period.
    (c) The “Highest Market Price Per Unit of Cryptocurrency During Quarter” represents the highest market price for a single unit of cryptocurrency reported on the Coinbase exchange during the respective quarter, without regard to when we obtained any of the cryptocurrency.
    (d) The “Market Value of Cryptocurrency Held at End of Quarter Using Highest Market Price” represents a mathematical calculation consisting of the highest market price for a single unit of cryptocurrency reported on the Coinbase exchange during the respective quarter multiplied by the number of cryptocurrency we held at the end of the applicable period.
    (e) The “Market Price Per Unit of Cryptocurrency at End of Quarter” represents the market price of a single unit of cryptocurrency on the Coinbase exchange at midnight UTC+8 time on the last day of the respective quarter, which aligns with our revenue recognition cut-off.
    (f) The “Market Value of Cryptocurrency Held at End of Quarter Using Ending Market Price” represents a mathematical calculation consisting of the market price of a single unit of cryptocurrency on the Coinbase exchange at midnight UTC+8 time on the last day of the respective quarter multiplied by the number of cryptocurrency we held at the end of the applicable period.

    The MIL Network

  • MIL-OSI: StepStone Group Reports Fourth Quarter and Fiscal Year 2025 Results

    Source: GlobeNewswire (MIL-OSI)

    NEW YORK, May 22, 2025 (GLOBE NEWSWIRE) — StepStone Group Inc. (Nasdaq: STEP), a global private markets investment firm focused on providing customized investment solutions and advisory and data services, today reported results for the quarter ended March 31, 2025. This represents results for the fourth quarter and fiscal year ended March 31, 2025. The Board of Directors of the Company has declared a quarterly cash dividend of $0.24 per share of Class A common stock, and a supplemental cash dividend of $0.40 per share of Class A common stock, both payable on June 30, 2025, to the holders of record as of the close of business on June 13, 2025.

    StepStone issued a full detailed presentation of its fourth quarter and full fiscal year ended March 31, 2025 results, which can be accessed by visiting the Company’s website at https://shareholders.stepstonegroup.com.

    Webcast and Earnings Conference Call

    Management will host a webcast and conference call today, Thursday, May 22, 2025 at 5:00 pm ET to discuss the Company’s results for the fourth quarter and fiscal year ended March 31, 2025. The webcast will be made available on the Shareholders section of the Company’s website at https://shareholders.stepstonegroup.com. To listen to a live broadcast, go to the site at least 15 minutes prior to the scheduled start time to register. A replay will also be available on the Shareholders section of the Company’s website approximately two hours after the conclusion of the event.

    To join as a live participant in the question and answer portion of the call, participants must register at https://register-conf.media-server.com/register/BI83b497f55a944def8cfadab7f935822b. Upon registering you will receive the dial-in number and a PIN to join the call as well as an email confirmation with the details.

    About StepStone

    StepStone Group Inc. (Nasdaq: STEP) is a global private markets investment firm focused on providing customized investment solutions and advisory and data services to its clients. As of March 31, 2025, StepStone was responsible for approximately $709 billion of total capital, including $189 billion of assets under management. StepStone’s clients include some of the world’s largest public and private defined benefit and defined contribution pension funds, sovereign wealth funds and insurance companies, as well as prominent endowments, foundations, family offices and private wealth clients, which include high-net-worth and mass affluent individuals. StepStone partners with its clients to develop and build private markets portfolios designed to meet their specific objectives across the private equity, infrastructure, private debt and real estate asset classes.

    Forward-Looking Statements

    Some of the statements in this release may constitute “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, Section 21E of the Securities Exchange Act of 1934 and the Private Securities Litigation Reform Act of 1995. All statements other than statements of historical fact are forward-looking. Words such as “anticipate,” “believe,” “continue,” “estimate,” “expect,” “future,” “intend,” “may,” “plan” and “will” and similar expressions identify forward-looking statements. Forward-looking statements reflect management’s current plans, estimates and expectations and are inherently uncertain. The inclusion of any forward-looking information in this release should not be regarded as a representation that the future plans, estimates or expectations contemplated will be achieved. Forward-looking statements are subject to various risks, uncertainties and assumptions. Important factors that could cause actual results to differ materially from those in forward-looking statements include, but are not limited to, global and domestic market and business conditions, our successful execution of business and growth strategies, the favorability of the private markets fundraising environment, successful integration of acquired businesses and regulatory factors relevant to our business, as well as assumptions relating to our operations, financial results, financial condition, business prospects, growth strategy and liquidity and the risks and uncertainties described in greater detail under the “Risk Factors” section of our annual report on Form 10-K filed with the U.S. Securities and Exchange Commission (the “SEC”) on May 24, 2024, and in our annual report on Form 10-K to be filed with the SEC for the fiscal year ended March 31, 2025, and in our subsequent reports filed with the SEC, as such factors may be updated from time to time. We undertake no obligation to revise or update any forward-looking statements, whether as a result of new information, future events or otherwise, except as may be required by law.

    Non-GAAP Financial Measures

    To supplement our consolidated financial statements, which are prepared and presented in accordance with generally accepted accounting principles in the United States (“GAAP”), we use the following non-GAAP financial measures: fee revenues, adjusted revenues, adjusted net income (on both a pre-tax and after-tax basis), adjusted net income per share, adjusted weighted-average shares, fee-related earnings, fee-related earnings margin, gross realized performance fees and performance fee-related earnings. We have provided this non-GAAP financial information, which is not calculated or presented in accordance with GAAP, as information supplemental and in addition to the financial measures presented in this earnings release that are calculated and presented in accordance with GAAP. Such non-GAAP financial measures should not be considered superior to, as a substitute for or alternative to, and should be considered in conjunction with, the GAAP financial measures presented in this earnings release. The presentation of these measures should not be construed as an inference that our future results will be unaffected by unusual or non-recurring items. In addition, the non-GAAP financial measures in this earnings release may not be comparable to similarly titled measures used by other companies in our industry or across different industries. For definitions of these non-GAAP measures and reconciliations to applicable GAAP measures, please see the section titled “Non-GAAP Financial Measures: Definitions and Reconciliations.”

    Financial Highlights and Key Business Drivers/Operating Metrics

      Three Months Ended   Year Ended March 31,   Percentage Change
    (in thousands, except share and per share amounts and where noted) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024     2025     vs. FQ4’24 vs. FY’24
    Financial Highlights                      
    GAAP Results                      
    Management and advisory fees, net $ 153,410   $ 178,015   $ 184,758   $ 190,840   $ 213,401     $ 585,140   $ 767,014     39% 31%
    Total revenues   356,810     186,401     271,677     339,023     377,729       711,631     1,174,830     6% 65%
    Total performance fees   203,400     8,386     86,919     148,183     164,328       126,491     407,816     (19)% 222%
    Net income (loss)   82,542     48,045     53,138     (287,163 )   13,153       167,820     (172,827 )   (84)% na
    Net income (loss) per share of Class A common stock:                      
    Basic $ 0.48   $ 0.20   $ 0.26   $ (2.61 ) $ (0.24 )   $ 0.91   $ (2.52 )   na na
    Diluted $ 0.48   $ 0.20   $ 0.26   $ (2.61 ) $ (0.24 )   $ 0.91   $ (2.52 )   na na
    Weighted-average shares of Class A common stock:                      
    Basic   64,194,859     66,187,754     68,772,051     73,687,289     75,975,770       63,489,135     71,142,916     18% 12%
    Diluted   67,281,567     68,593,761     69,695,315     73,687,289     75,975,770       66,544,038     71,142,916     13% 7%
    Quarterly dividend per share of Class A common stock(1) $ 0.21   $ 0.21   $ 0.24   $ 0.24   $ 0.24     $ 0.83   $ 0.93     14% 12%
    Supplemental dividend per share of Class A common stock(2) $   $ 0.15   $   $   $     $ 0.25   $ 0.15     na (40)%
    Accrued carried interest allocations $ 1,354,051   $ 1,328,853   $ 1,381,110   $ 1,474,543   $ 1,495,664           10%  
                           
    Non-GAAP Results(3)                      
    Fee revenues(4) $ 153,808   $ 178,514   $ 185,481   $ 191,832   $ 214,662     $ 586,379   $ 770,489     40% 31%
    Adjusted revenues   177,357     221,165     208,788     243,905     295,861       665,060     969,719     67% 46%
    Fee-related earnings (“FRE”)   50,900     71,656     72,349     74,118     94,081       189,793     312,204     85% 64%
    FRE margin(5)   33 %   40 %   39 %   39 %   44 %     32 %   41 %      
    Gross realized performance fees   23,549     42,651     23,307     52,073     81,199       78,681     199,230     245% 153%
    Performance fee-related earnings (“PRE”)   12,128     21,803     14,540     26,596     41,543       40,994     104,482     243% 155%
    Adjusted net income (“ANI”)   37,716     57,241     53,569     52,659     80,603       139,393     244,072     114% 75%
    Adjusted weighted-average shares   115,512,301     118,510,499     118,774,233     118,935,179     118,869,111       115,134,473     118,772,442        
    ANI per share $ 0.33   $ 0.48   $ 0.45   $ 0.44   $ 0.68     $ 1.21   $ 2.05     106% 69%
                           
    Key Business Drivers/Operating Metrics (in billions)                      
    Assets under management (“AUM”)(6) $ 156.6   $ 169.3   $ 176.1   $ 179.2   $ 189.4           21%  
    Assets under advisement (“AUA”)(6)   521.1     531.4     505.9     518.7     519.7            
    Fee-earning AUM (“FEAUM”)   93.9     100.4     104.4     114.2     121.4           29%  
    Undeployed fee-earning capital (“UFEC”)   22.6     27.6     29.7     21.7     24.6           9%  

    _______________________________
    (1) Dividends paid, as reported in this table, relate to the preceding quarterly period in which they were earned.
    (2) The supplemental cash dividend relates to earnings in respect of our full fiscal years 2023 and 2024, respectively.
    (3) Fee revenues, adjusted revenues, FRE, FRE margin, gross realized performance fees, PRE, ANI, adjusted weighted-average shares and ANI per share are non-GAAP measures. See the definitions of these measures and reconciliations to the respective, most comparable GAAP measures under “Non-GAAP Financial Measures: Definitions and Reconciliations.”
    (4) Excludes the impact of consolidating the Consolidated Funds. See reconciliation of GAAP measures to adjusted measures that follows.
    (5) FRE margin is calculated by dividing FRE by fee revenues.
    (6) AUM/AUA reflects final data for the prior period, adjusted for net new client account activity through the period presented. Does not include post-period investment valuation or cash activity. Net asset value (“NAV”) data for underlying investments is as of the prior period, as reported by underlying managers up to the business day occurring on or after 100 days, or 115 days at the fiscal year-end, following the prior period end. When NAV data is not available by the business day occurring on or after 100 days, or 115 days at the fiscal year-end, following the prior period end, such NAVs are adjusted for cash activity following the last available reported NAV.  

    StepStone Group Inc.
    GAAP Consolidated Balance Sheets
    (in thousands, except share and per share amounts)

      As of March 31,
        2025       2024
    Assets      
    Cash and cash equivalents $ 244,791     $ 143,430
    Restricted cash   502       718
    Fees and accounts receivable   80,871       56,769
    Due from affiliates   92,723       67,531
    Investments:      
    Investments in funds   183,694       135,043
    Accrued carried interest allocations   1,495,664       1,354,051
    Legacy Greenspring investments in funds and accrued carried interest allocations(1)   629,228       631,197
    Deferred income tax assets   382,886       184,512
    Lease right-of-use assets, net   91,841       97,763
    Other assets and receivables   62,869       60,611
    Intangibles, net   263,872       304,873
    Goodwill   580,542       580,542
    Assets of Consolidated Funds:      
    Cash and cash equivalents   44,511       38,164
    Investments, at fair value   415,011       131,858
    Other assets   17,688       1,745
    Total assets $ 4,586,693     $ 3,788,807
    Liabilities and stockholders’ equity      
    Accounts payable, accrued expenses and other liabilities $ 89,731     $ 127,417
    Accrued compensation and benefits   736,695       101,481
    Accrued carried interest-related compensation   757,968       719,497
    Legacy Greenspring accrued carried interest-related compensation(1)   495,739       484,154
    Due to affiliates   331,821       212,918
    Lease liabilities   113,519       119,739
    Debt obligations   269,268       148,822
    Liabilities of Consolidated Funds:      
    Other liabilities   17,580       1,645
    Total liabilities   2,812,321       1,915,673
    Redeemable non-controlling interests in Consolidated Funds   377,897       102,623
    Redeemable non-controlling interests in subsidiaries   6,327       115,920
    Stockholders’ equity:      
    Class A common stock, $0.001 par value, 650,000,000 authorized; 76,761,399 and 65,614,902 issued and outstanding as of March 31, 2025 and 2024, respectively   77       66
    Class B common stock, $0.001 par value, 125,000,000 authorized; 39,656,954 and 45,030,959 issued and outstanding as of March 31, 2025 and 2024, respectively   40       45
    Additional paid-in capital   421,057       310,293
    Retained earnings (accumulated deficit)   (242,546 )     13,768
    Accumulated other comprehensive income   728       304
    Total StepStone Group Inc. stockholders’ equity   179,356       324,476
    Non-controlling interests in subsidiaries   1,056,510       974,559
    Non-controlling interests in legacy Greenspring entities(1)   133,489       147,042
    Non-controlling interests in the Partnership   20,793       208,514
    Total stockholders’ equity   1,390,148       1,654,591
    Total liabilities and stockholders’ equity $ 4,586,693     $ 3,788,807

    (1)   Reflects amounts attributable to consolidated VIEs for which the Company did not acquire any direct economic interests.     

    StepStone Group Inc.
    GAAP Consolidated Statements of Income (Loss)
    (in thousands, except share and per share amounts)

      Three Months Ended March 31,   Year Ended March 31,
        2025       2024       2025       2024  
    Revenues              
    Management and advisory fees, net $ 213,401     $ 153,410     $ 767,014     $ 585,140  
    Performance fees:              
    Incentive fees   5,910       2,496       32,275       25,339  
    Carried interest allocations:              
    Realized   75,935       18,054       159,653       49,401  
    Unrealized   21,177       151,757       141,547       126,908  
    Total carried interest allocations   97,112       169,811       301,200       176,309  
    Legacy Greenspring carried interest allocations(1)   61,306       31,093       74,341       (75,157 )
    Total performance fees   164,328       203,400       407,816       126,491  
    Total revenues   377,729       356,810       1,174,830       711,631  
    Expenses              
    Compensation and benefits:              
    Cash-based compensation   85,510       74,411       331,808       292,962  
    Equity-based compensation   126,197       13,937       669,126       42,357  
    Performance fee-related compensation:              
    Realized   39,656       11,421       94,748       37,687  
    Unrealized   27,777       84,014       94,272       74,694  
    Total performance fee-related compensation   67,433       95,435       189,020       112,381  
    Legacy Greenspring performance fee-related compensation(1)   61,306       31,093       74,341       (75,157 )
    Total compensation and benefits   340,446       214,876       1,264,295       372,543  
    General, administrative and other   43,152       54,310       177,354       167,317  
    Total expenses   383,598       269,186       1,441,649       539,860  
    Other income (expense)              
    Investment income   9,386       3,337       15,096       7,452  
    Legacy Greenspring investment income (loss)(1)   2,934       (33 )     (1,185 )     (9,087 )
    Investment income of Consolidated Funds   34,496       6,115       65,374       28,472  
    Interest income   3,218       1,429       10,850       3,664  
    Interest expense   (3,191 )     (2,649 )     (12,701 )     (9,331 )
    Other income (loss)   (31,024 )     (1,308 )     (32,650 )     2,455  
    Total other income   15,819       6,891       44,784       23,625  
    Income (loss) before income tax   9,950       94,515       (222,035 )     195,396  
    Income tax expense (benefit)   (3,203 )     11,973       (49,208 )     27,576  
    Net income (loss)   13,153       82,542       (172,827 )     167,820  
    Less: Net income attributable to non-controlling interests in subsidiaries   16,316       4,443       79,282       37,240  
    Less: Net income (loss) attributable to non-controlling interests in legacy Greenspring entities(1)   2,934       (33 )     (1,185 )     (9,087 )
    Less: Net income (loss) attributable to non-controlling interests in the Partnership   (17,994 )     37,279       (125,850 )     59,956  
    Less: Net income attributable to redeemable non-controlling interests in Consolidated Funds   30,630       4,248       53,731       15,838  
    Less: Net income (loss) attributable to redeemable non-controlling interests in subsidiaries   (225 )     5,782       758       5,782  
    Net income (loss) attributable to StepStone Group Inc. $ (18,508 )   $ 30,823     $ (179,563 )   $ 58,091  
    Net income (loss) per share of Class A common stock:              
    Basic $ (0.24 )   $ 0.48     $ (2.52 )   $ 0.91  
    Diluted $ (0.24 )   $ 0.48     $ (2.52 )   $ 0.91  
    Weighted-average shares of Class A common stock:              
    Basic   75,975,770       64,194,859       71,142,916       63,489,135  
    Diluted   75,975,770       67,281,567       71,142,916       66,544,038  

    (1) Reflects amounts attributable to consolidated VIEs for which the Company did not acquire any direct economic interests.  

    Non-GAAP Financial Measures: Definitions and Reconciliations

    Fee Revenues

    Fee revenues represents management and advisory fees, net, including amounts earned from the Consolidated Funds which are eliminated in consolidation. We believe fee revenues is useful to investors because it presents the net amount of management and advisory fee revenues attributable to us.

    The table below presents the components of fee revenues.

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024   2025
    Focused commingled funds(1)(2) $ 80,434 $ 104,798 $ 107,855 $ 105,718 $ 124,604   $ 296,667 $ 442,975
    Separately managed accounts   55,945   57,376   61,393   66,245   67,695     223,958   252,709
    Advisory and other services   16,147   14,769   14,907   17,458   19,927     60,057   67,061
    Fund reimbursement revenues(1)   1,282   1,571   1,326   2,411   2,436     5,697   7,744
    Fee revenues $ 153,808 $ 178,514 $ 185,481 $ 191,832 $ 214,662   $ 586,379 $ 770,489

    _______________________________
    (1) Reflects the add-back of management and advisory fee revenues for the Consolidated Funds, which have been eliminated in consolidation.
    (2) Includes income-based incentive fees from certain funds:

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024   2025
    Income-based incentive fees $ 753 $ 1,113 $ 1,347 $ 2,120 $ 3,377   $ 1,372 $ 7,956


    Adjusted Revenues

    Adjusted revenues represents the components of revenues used in the determination of ANI and comprise fee revenues, adjusted incentive fees and realized carried interest allocations. We believe adjusted revenues is useful to investors because it presents a measure of realized revenues.

    The table below shows a reconciliation of revenues to adjusted revenues.

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March
    31, 2025
        2024     2025  
    Total revenues $ 356,810   $ 186,401 $ 271,677   $ 339,023   $ 377,729     $ 711,631   $ 1,174,830  
    Unrealized carried interest allocations   (151,757 )   25,170   (52,215 )   (93,325 )   (21,177 )     (126,908 )   (141,547 )
    Deferred incentive fees   1,450     6   2,445         (513 )     2,392     1,938  
    Legacy Greenspring carried interest allocations   (31,093 )   9,089   (13,917 )   (8,207 )   (61,306 )     75,157     (74,341 )
    Management and advisory fee revenues for the Consolidated Funds(1)   398     499   723     992     1,261       1,239     3,475  
    Incentive fees for the Consolidated Funds(2)   1,549       75     5,422     (133 )     1,549     5,364  
    Adjusted revenues $ 177,357   $ 221,165 $ 208,788   $ 243,905   $ 295,861     $ 665,060   $ 969,719  

    _______________________________
    (1) Reflects the add-back of management and advisory fee revenues for the Consolidated Funds, which have been eliminated in consolidation.
    (2) Reflects the add back of incentive fees for the Consolidated Funds, which have been eliminated in consolidation.

    Adjusted Net Income

    Adjusted net income, or “ANI,” is a non-GAAP performance measure that we present before the consolidation of StepStone Funds on a pre-tax and after-tax basis used to evaluate profitability. ANI represents the after-tax net realized income attributable to us. ANI does not reflect legacy Greenspring carried interest allocation revenues, legacy Greenspring carried interest-related compensation and legacy Greenspring investment income (loss) as none of the economics are attributable to us. The components of revenues used in the determination of ANI (“adjusted revenues”) comprise fee revenues, adjusted incentive fees and realized carried interest allocations. In addition, ANI excludes: (a) unrealized carried interest allocation revenues and related compensation, (b) unrealized investment income (loss), (c) equity-based compensation for awards granted prior to and in connection with our IPO, profits interests issued by our non-wholly owned subsidiaries, and unrealized mark-to-market changes in the fair value of the profits interests issued in the private wealth subsidiary, (d) amortization of intangibles, (e) net income (loss) attributable to non-controlling interests in our subsidiaries and realized gains attributable to the profits interests issued in the private wealth subsidiary, (f) charges associated with acquisitions and corporate transactions, and (g) certain other items that we believe are not indicative of our core operating performance (as listed in the table below). ANI is fully taxed at our blended statutory rate. We believe ANI and adjusted revenues are useful to investors because they enable investors to evaluate the performance of our business across reporting periods.

    Fee-Related Earnings

    Fee-related earnings, or “FRE,” is a non-GAAP performance measure used to monitor our baseline earnings from recurring management and advisory fees. FRE is a component of ANI and comprises fee revenues less adjusted expenses which are operating expenses other than (a) performance fee-related compensation, (b) equity-based compensation for awards granted prior to and in connection with our IPO, profits interests issued by our non-wholly owned subsidiaries, and unrealized mark-to-market changes in the fair value of the profits interests issued in the private wealth subsidiary, (c) amortization of intangibles, (d) charges associated with acquisitions and corporate transactions, and (e) certain other items that we believe are not indicative of our core operating performance (as listed in the table below). FRE is presented before income taxes. We believe FRE is useful to investors because it provides additional insight into the operating profitability of our business and our ability to cover direct base compensation and operating expenses from total fee revenue.

    The table below shows a reconciliation of GAAP measures to additional non-GAAP measures. We use the non-GAAP measures presented below as components when calculating FRE and ANI (as defined below). We believe these additional non-GAAP measures are useful to investors in evaluating both the baseline earnings from recurring management and advisory fees, which provide additional insight into the operating profitability of our business, and the after-tax net realized income attributable to us, allowing investors to evaluate the performance of our business. These additional non-GAAP measures remove the impact of Consolidated Funds that we are required to consolidate under GAAP, and certain other items that we believe are not indicative of our core operating performance.

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024     2025  
    GAAP management and advisory fees, net $ 153,410   $ 178,015   $ 184,758   $ 190,840   $ 213,401     $ 585,140   $ 767,014  
    Management and advisory fee revenues for the Consolidated Funds(1)   398     499     723     992     1,261       1,239     3,475  
    Fee revenues $ 153,808   $ 178,514   $ 185,481   $ 191,832   $ 214,662     $ 586,379   $ 770,489  
                     
    GAAP incentive fees $ 2,496   $ 841   $ 3,155   $ 22,369   $ 5,910     $ 25,339   $ 32,275  
    Adjustments(2)   2,999     6     2,520     5,422     (646 )     3,941     7,302  
    Adjusted incentive fees $ 5,495   $ 847   $ 5,675   $ 27,791   $ 5,264     $ 29,280   $ 39,577  
                     
    GAAP cash-based compensation $ 74,411   $ 78,224   $ 82,871   $ 85,203   $ 85,510     $ 292,962   $ 331,808  
    Adjustments(3)   (461 )   (428 )   (285 )   339           (2,140 )   (374 )
    Adjusted cash-based compensation $ 73,950   $ 77,796   $ 82,586   $ 85,542   $ 85,510     $ 290,822   $ 331,434  
                     
    GAAP equity-based compensation $ 13,937   $ 19,179   $ 37,332   $ 486,418   $ 126,197     $ 42,357   $ 669,126  
    Adjustments(4)   (12,210 )   (16,785 )   (34,947 )   (483,958 )   (123,263 )     (36,635 )   (658,953 )
    Adjusted equity-based compensation $ 1,727   $ 2,394   $ 2,385   $ 2,460   $ 2,934     $ 5,722   $ 10,173  
                     
    GAAP general, administrative and other $ 54,310   $ 41,011   $ 50,061   $ 43,130   $ 43,152     $ 167,317   $ 177,354  
    Adjustments(5)   (27,079 )   (14,343 )   (21,900 )   (13,418 )   (11,015 )     (67,275 )   (60,676 )
    Adjusted general, administrative and other $ 27,231   $ 26,668   $ 28,161   $ 29,712   $ 32,137     $ 100,042   $ 116,678  
                     
    GAAP interest income $ 1,429   $ 2,057   $ 3,016   $ 2,559   $ 3,218     $ 3,664   $ 10,850  
    Interest income earned by the Consolidated Funds(6)   (612 )   (907 )   (1,363 )   (887 )   (1,600 )     (1,645 )   (4,757 )
    Adjusted interest income $ 817   $ 1,150   $ 1,653   $ 1,672   $ 1,618     $ 2,019   $ 6,093  
                     
    GAAP other income (loss) $ (1,308 ) $ (351 ) $ 1,177   $ (2,452 ) $ (31,024 )   $ 2,455   $ (32,650 )
    Adjustments(7)   395     (72 )   (1,082 )   1,883     30,606       (3,879 )   31,335  
    Adjusted other income (loss) $ (913 ) $ (423 ) $ 95   $ (569 ) $ (418 )   $ (1,424 ) $ (1,315 )

    ______________________________
    (1) Reflects the add-back of management and advisory fee revenues for the Consolidated Funds, which have been eliminated in consolidation.
    (2) Reflects the add back of incentive fee revenues for the Consolidated Funds, which have been eliminated in consolidation, and deferred incentive fees that are not included in GAAP revenues.
    (3) Reflects the removal of compensation paid to certain employees as part of an acquisition earn-out and unrealized amounts associated with cash-based incentive awards tracked to the performance of a designated investment fund.
    (4) Reflects the removal of equity-based compensation for awards granted prior to and in connection with the IPO, profits interests issued by our non-wholly owned subsidiaries, and unrealized mark-to-market changes in the fair value of the profits interests issued in the private wealth subsidiary.
    (5) Reflects the removal of lease remeasurement adjustments, accelerated depreciation of leasehold improvements for changes in lease terms, amortization of intangibles, transaction-related costs, unrealized mark-to-market changes in fair value for contingent consideration obligation and other non-core operating income and expenses.
    (6) Reflects the removal of interest income earned by the Consolidated Funds.
    (7) Reflects the removal of amounts for Tax Receivable Agreements adjustments recognized as other income (loss), loss associated with payment made in connection with a secondary transaction executed by one of our private wealth funds, gain associated with amounts received as part of negotiations with a third party related to certain corporate matters, loss on sale of subsidiary and the impact of consolidation of the Consolidated Funds.

    The table below shows a reconciliation of income (loss) before income tax to ANI and FRE.

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024     2025  
    Income (loss) before income tax $ 94,515     54,842   $ 57,888   $ (344,715 ) $ 9,950     $ 195,396   $ (222,035 )
    Net income attributable to non-controlling interests in subsidiaries(1)   (12,822 )   (18,951 )   (17,812 )   (32,765 )   (33,369 )     (49,220 )   (102,897 )
    Net (income) loss attributable to non-controlling interests in legacy Greenspring entities   33     1,255     4,031     (1,167 )   (2,934 )     9,087     1,185  
    Unrealized carried interest allocations   (151,757 )   25,170     (52,215 )   (93,325 )   (21,177 )     (126,908 )   (141,547 )
    Unrealized performance fee-related compensation   84,014     (10,923 )   27,748     49,670     27,777       74,694     94,272  
    Unrealized investment (income) loss   (2,280 )   (1,180 )   (430 )   656     (6,007 )     (907 )   (6,961 )
    Impact of Consolidated Funds   (4,138 )   (7,731 )   (9,267 )   (6,892 )   (35,723 )     (26,076 )   (59,613 )
    Deferred incentive fees   1,450     6     2,445         (513 )     2,392     1,938  
    Equity-based compensation(2)   12,210     16,785     34,947     483,958     123,263       36,635     658,953  
    Amortization of intangibles   10,423     10,250     10,250     10,250     10,250       42,406     41,000  
    Tax Receivable Agreements adjustments through earnings   90                 (348 )     312     (348 )
    Non-core items(3)   16,780     4,137     11,349     2,094     32,474       21,565     50,054  
    Pre-tax ANI   48,518     73,660     68,934     67,764     103,643       179,376     314,001  
    Income taxes(4)   (10,802 )   (16,419 )   (15,365 )   (15,105 )   (23,040 )     (39,983 )   (69,929 )
    ANI   37,716     57,241     53,569     52,659     80,603       139,393     244,072  
    Income taxes(4)   10,802     16,419     15,365     15,105     23,040       39,983     69,929  
    Realized carried interest allocations   (18,054 )   (41,804 )   (17,632 )   (24,282 )   (75,935 )     (49,401 )   (159,653 )
    Realized performance fee-related compensation   11,421     20,848     8,767     25,477     39,656       37,687     94,748  
    Realized investment income   (1,057 )   (1,415 )   (1,621 )   (1,720 )   (3,379 )     (6,545 )   (8,135 )
    Adjusted incentive fees(5)   (5,495 )   (847 )   (5,675 )   (27,791 )   (5,264 )     (29,280 )   (39,577 )
    Adjusted interest income(5)   (817 )   (1,150 )   (1,653 )   (1,672 )   (1,618 )     (2,019 )   (6,093 )
    Interest expense   2,649     2,990     3,512     3,008     3,191       9,331     12,701  
    Adjusted other (income) loss(5)(6)   913     423     (95 )   569     418       1,424     1,315  
    Net income attributable to non-controlling interests in subsidiaries(1)   12,822     18,951     17,812     32,765     33,369       49,220     102,897  
    FRE $ 50,900   $ 71,656   $ 72,349   $ 74,118   $ 94,081     $ 189,793   $ 312,204  

    _______________________________
    (1) Reflects the portion of pre-tax ANI attributable to non-controlling interests in our subsidiaries and realized gains attributable to the profits interests issued in the private wealth subsidiary:

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024   2025
    FRE attributable to non-controlling interests in subsidiaries and profits interests $ 11,559 $ 13,308 $ 14,969 $ 21,063 $ 30,451   $ 42,074 $ 79,791
    Performance related earnings / other income (loss) attributable to non-controlling interests in subsidiaries and profits interests   1,263   5,643   2,843   11,702   2,918     7,146   23,106
    Net income attributable to non-controlling interests in subsidiaries and profits interests $ 12,822 $ 18,951 $ 17,812 $ 32,765 $ 33,369   $ 49,220 $ 102,897

    The contribution to pre-tax ANI attributable to non-controlling interests in subsidiaries and profits interests and performance related earnings / other income (loss) attributable to non-controlling interests in subsidiaries and profits interests presented above specifically related to the profits interests issued in the private wealth subsidiary is presented below.

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024   2025
    FRE attributable to profits interests issued in the private wealth subsidiary $ $ 574 $ 2,051 $ 2,956 $ 6,399     $ $ 11,980
    Performance related earnings / other income (loss) attributable to profits interests issued in the private wealth subsidiary     51   206   11,137   (224 )     3,074   11,170
    Net income attributable to profits interests issued in the private wealth subsidiary $ $ 625 $ 2,257 $ 14,093 $ 6,175     $ 3,074 $ 23,150

    The contribution to pre-tax ANI attributable to non-controlling interests in subsidiaries and performance related earnings / other income (loss) attributable to non-controlling interests in subsidiaries presented above specifically not attributable to the profits interests issued in the private wealth subsidiary is presented below.

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024   2025
    FRE attributable to non-controlling interests in subsidiaries $ 11,559 $ 12,734 $ 12,918 $ 18,107 $ 24,052   $ 42,074 $ 67,811
    Performance related earnings / other income (loss) attributable to non-controlling interests in subsidiaries   1,263   5,592   2,637   565   3,142     4,072   11,936
    Net income attributable to non-controlling interests in subsidiaries $ 12,822 $ 18,326 $ 15,555 $ 18,672 $ 27,194   $ 46,146 $ 79,747

    (2) Reflects equity-based compensation for awards granted prior to and in connection with the IPO, profits interests issued by our non-wholly owned subsidiaries, and unrealized mark-to-market changes in the fair value of the profits interests issued in the private wealth subsidiary.
    (3) Includes (income) expense related to the following non-core operating income and expenses:

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024     2025
    Transaction costs $ 3,985 $ 672 $ 140 $ 12   $ 179     $ 4,855   $ 1,003
    Lease remeasurement adjustments                   (106 )  
    Accelerated depreciation of leasehold improvements for changes in lease terms                   1,893    
    (Gain) loss on change in fair value for contingent consideration obligation   12,280   2,953   10,888   2,476     (205 )     17,217     16,112
    Compensation paid to certain employees as part of an acquisition earn-out   515   482   321   (394 )         2,194     409
    Loss on payment made in connection with private wealth fund secondary transaction             32,500           32,500
    Gain from negotiation of certain corporate matters                   (5,300 )  
    Loss on sale of subsidiary                   812    
    Other non-core items     30                   30
    Total non-core operating income and expenses $ 16,780 $ 4,137 $ 11,349 $ 2,094   $ 32,474     $ 21,565   $ 50,054

    (4) Represents corporate income taxes at a blended statutory rate applied to pre-tax ANI:

      Three Months Ended   Year Ended March 31,
      March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
      2024   2025  
    Federal statutory rate 21.0% 21.0% 21.0% 21.0% 21.0%   21.0%   21.0%  
    Combined state, local and foreign rate 1.3% 1.3% 1.3% 1.3% 1.2%   1.3%   1.3%  
    Blended statutory rate 22.3% 22.3% 22.3% 22.3% 22.2%   22.3%   22.3%  

    (5) Excludes the impact of consolidating the Consolidated Funds and includes deferred incentive fees which are not included in GAAP revenues.
    (6) Excludes amounts for Tax Receivable Agreements adjustments recognized as other income (loss) ($0.3 million for the three months ended March 31, 2025, $(0.1) million for the three months ended March 31, 2024, and $0.3 million and $(0.3) million in fiscal 2025 and fiscal 2024, respectively), loss associated with payment made in connection with a secondary transaction executed by one of our private wealth funds ($32.5 million for the three months ended March 31, 2025 and in fiscal 2025), gain associated with amounts received as part of negotiations with a third party related to certain corporate matters ($5.3 million in fiscal 2024), and loss on sale of subsidiary ($0.8 million in fiscal 2024).

    Fee-Related Earnings Margin

    FRE margin is a non-GAAP performance measure which is calculated by dividing FRE by fee revenues. We believe FRE margin is an important measure of profitability on revenues that are largely recurring by nature. We believe FRE margin is useful to investors because it enables them to better evaluate the operating profitability of our business across periods.

    The table below shows a reconciliation of FRE to FRE margin.

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024     2025  
    FRE $ 50,900   $ 71,656   $ 72,349   $ 74,118   $ 94,081     $ 189,793   $ 312,204  
    Fee revenues   153,808     178,514     185,481     191,832     214,662       586,379     770,489  
    FRE margin   33 %   40 %   39 %   39 %   44 %     32 %   41 %


    Gross Realized Performance Fees

    Gross realized performance fees represents realized carried interest allocations and adjusted incentive fees. We believe gross realized performance fees is useful to investors because it presents the total performance fees realized by us.

    Performance Fee-Related Earnings

    Performance fee-related earnings, or “PRE,” represents gross realized performance fees less realized performance fee-related compensation. We believe PRE is useful to investors because it presents the performance fees attributable to us, net of amounts paid to employees as performance fee-related compensation.

    The table below shows a reconciliation of total performance fees to gross realized performance fees and PRE.

      Three Months Ended   Year Ended March 31,
    (in thousands) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024     2025  
    Incentive fees $ 2,496   $ 841   $ 3,155   $ 22,369   $ 5,910     $ 25,339   $ 32,275  
    Realized carried interest allocations   18,054     41,804     17,632     24,282     75,935       49,401     159,653  
    Unrealized carried interest allocations   151,757     (25,170 )   52,215     93,325     21,177       126,908     141,547  
    Legacy Greenspring carried interest allocations   31,093     (9,089 )   13,917     8,207     61,306       (75,157 )   74,341  
    Total performance fees   203,400     8,386     86,919     148,183     164,328       126,491     407,816  
    Unrealized carried interest allocations   (151,757 )   25,170     (52,215 )   (93,325 )   (21,177 )     (126,908 )   (141,547 )
    Legacy Greenspring carried interest allocations   (31,093 )   9,089     (13,917 )   (8,207 )   (61,306 )     75,157     (74,341 )
    Incentive fee revenues for the Consolidated Funds(1)   1,549         75     5,422     (133 )     1,549     5,364  
    Deferred incentive fees   1,450     6     2,445         (513 )     2,392     1,938  
    Gross realized performance fees   23,549     42,651     23,307     52,073     81,199       78,681     199,230  
    Realized performance fee-related compensation   (11,421 )   (20,848 )   (8,767 )   (25,477 )   (39,656 )     (37,687 )   (94,748 )
    PRE $ 12,128   $ 21,803   $ 14,540   $ 26,596   $ 41,543     $ 40,994   $ 104,482  

    _______________________________
    (1) Reflects the add back of incentive fee revenues for the Consolidated Funds, which have been eliminated in consolidation.

    Adjusted Weighted-Average Shares and Adjusted Net Income Per Share

    ANI per share measures our per-share earnings assuming all Class B units, Class C units and Class D units in the Partnership were exchanged for Class A common stock in SSG, including the dilutive impact of outstanding equity-based awards. ANI per share is calculated as ANI divided by adjusted weighted-average shares outstanding. We believe adjusted weighted-average shares and ANI per share are useful to investors because they enable investors to better evaluate per-share operating performance across reporting periods.

    The following table shows a reconciliation of diluted weighted-average shares of Class A common stock outstanding to adjusted weighted-average shares outstanding used in the computation of ANI per share.

      Three Months Ended   Year Ended March 31,
      March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024   2025
    ANI $ 37,716 $ 57,241 $ 53,569 $ 52,659 $ 80,603   $ 139,393 $ 244,072
                     
    Weighted-average shares of Class A common stock outstanding – Basic   64,194,859   66,187,754   68,772,051   73,687,289   75,975,770     63,489,135   71,142,916
    Assumed vesting of RSUs   512,946   673,854   921,166   491,014   270,492     512,152   590,645
    Assumed vesting and exchange of Class B2 units   2,573,762   1,732,153           2,542,751   431,851
    Assumed purchase under ESPP       2,098           529
    Exchange of Class B units in the Partnership(1)   46,272,227   45,827,707   45,212,921   41,729,937   40,122,028     46,356,244   43,233,005
    Exchange of Class C units in the Partnership(1)   1,958,507   1,849,846   1,626,812   1,016,737   965,761     2,234,191   1,365,647
    Exchange of Class D units in the Partnership(1)     2,239,185   2,239,185   2,010,202   1,535,060       2,007,849
    Adjusted weighted-average shares   115,512,301   118,510,499   118,774,233   118,935,179   118,869,111     115,134,473   118,772,442
                     
    ANI per share $ 0.33 $ 0.48 $ 0.45 $ 0.44 $ 0.68   $ 1.21 $ 2.05

    _______________________________
    (1)   Assumes the full exchange of Class B units, Class C units or Class D units in the Partnership for Class A common stock of SSG pursuant to the Class B Exchange Agreement, Class C Exchange Agreement or Class D Exchange Agreement, respectively.

    Key Operating Metrics

    We monitor certain operating metrics that are either common to the asset management industry or that we believe provide important data regarding our business. Refer to the Glossary below for a definition of each of these metrics.

    Fee-Earning AUM

      Three Months Ended   Year Ended March 31,   Percentage
    Change
    (in millions) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
        2024     2025     vs. FQ4’24
    Separately Managed Accounts                    
    Beginning balance $ 56,660   $ 58,897   $ 60,272   $ 62,121   $ 69,974     $ 55,345   $ 58,897     23%
    Contributions(1)   2,757     2,085     1,723     9,033     3,874       6,327     16,715     41%
    Distributions(2)   (795 )   (830 )   (535 )   (1,000 )   (1,225 )     (4,080 )   (3,590 )   54%
    Market value, FX and other(3)   275     120     661     (180 )   551       1,305     1,152     100%
    Ending balance $ 58,897   $ 60,272   $ 62,121   $ 69,974   $ 73,174     $ 58,897   $ 73,174     24%
                         
    Focused Commingled Funds                    
    Beginning balance $ 32,772   $ 34,961   $ 40,084   $ 42,294   $ 44,192     $ 30,086   $ 34,961     35%
    Contributions(1)   2,429     5,653     2,122     2,520     3,403       6,115     13,698     40%
    Distributions(2)   (327 )   (661 )   (282 )   (682 )   (313 )     (1,841 )   (1,938 )   (4)%
    Market value, FX and other(3)   87     131     370     60     934       601     1,495     974%
    Ending balance $ 34,961   $ 40,084   $ 42,294   $ 44,192   $ 48,216     $ 34,961   $ 48,216     38%
                         
    Total                    
    Beginning balance $ 89,432   $ 93,858   $ 100,356   $ 104,415   $ 114,166     $ 85,431   $ 93,858     28%
    Contributions(1)   5,186     7,738     3,845     11,553     7,277       12,442     30,413     40%
    Distributions(2)   (1,122 )   (1,491 )   (817 )   (1,682 )   (1,538 )     (5,921 )   (5,528 )   37%
    Market value, FX and other(3)   362     251     1,031     (120 )   1,485       1,906     2,647     310%
    Ending balance $ 93,858   $ 100,356   $ 104,415   $ 114,166   $ 121,390     $ 93,858   $ 121,390     29%

    _______________________________
    (1) Contributions consist of new capital commitments that earn fees on committed capital and capital contributions to funds and accounts that earn fees on net invested capital or NAV.
    (2) Distributions consist of returns of capital from funds and accounts that pay fees on net invested capital or NAV and reductions in fee-earning AUM from funds that moved from a committed capital to net invested capital fee basis or from funds and accounts that no longer pay fees.
    (3) Market value, FX and other primarily consist of changes in market value appreciation (depreciation) for funds that pay on NAV and the effect of foreign exchange rate changes on non-U.S. dollar denominated commitments. The three months ended March 31, 2025 and year ended March 31, 2025 include a $0.6 billion secondary transaction within focused commingled funds.    

    Asset Class Summary

      Three Months Ended   Percentage
    Change
    (in millions) March 31,
    2024
    June 30,
    2024
    September
    30, 2024
    December
    31, 2024
    March 31,
    2025
      vs. FQ4’24
    FEAUM              
    Private equity $ 49,869 $ 54,855 $ 57,136 $ 62,811 $ 65,007   30%
    Infrastructure   20,114   20,377   20,986   23,411   23,830   18%
    Private debt   15,477   16,161   16,975   17,882   19,517   26%
    Real estate   8,398   8,963   9,318   10,062   13,036   55%
    Total $ 93,858 $ 100,356 $ 104,415 $ 114,166 $ 121,390   29%
                   
    Separately managed accounts $ 58,897 $ 60,272 $ 62,121 $ 69,974 $ 73,174   24%
    Focused commingled funds   34,961   40,084   42,294   44,192   48,216   38%
    Total $ 93,858 $ 100,356 $ 104,415 $ 114,166 $ 121,390   29%
                   
    AUM(1)              
    Private equity $ 81,942 $ 89,329 $ 91,891 $ 93,404 $ 95,937   17%
    Infrastructure   30,003   32,756   35,392   36,156   37,026   23%
    Private debt   28,491   30,336   31,854   31,987   37,133   30%
    Real estate   16,201   16,912   16,996   17,665   19,284   19%
    Total $ 156,637 $ 169,333 $ 176,133 $ 179,212 $ 189,380   21%
                   
    Separately managed accounts $ 93,938 $ 103,003 $ 107,252 $ 109,305 $ 114,806   22%
    Focused commingled funds   48,545   51,682   53,870   55,142   59,410   22%
    Advisory AUM   14,154   14,648   15,011   14,765   15,164   7%
    Total $ 156,637 $ 169,333 $ 176,133 $ 179,212 $ 189,380   21%
                   
    AUA              
    Private equity $ 270,350 $ 279,909 $ 255,125 $ 263,420 $ 262,884   (3)%
    Infrastructure   60,339   62,599   62,891   67,100   69,027   14%
    Private debt   21,976   22,280   19,328   19,325   19,726   (10)%
    Real estate   168,455   166,659   168,519   168,807   168,047   —%
    Total $ 521,120 $ 531,447 $ 505,863 $ 518,652 $ 519,684   —%
                   
    Total capital responsibility(2) $ 677,757 $ 700,780 $ 681,996 $ 697,864 $ 709,064   5%

    _____________________________
    Note: Amounts may not sum to total due to rounding. AUM/AUA reflects final data for the prior period, adjusted for net new client account activity through the period presented, and does not include post-period investment valuation or cash activity. Net asset value (“NAV”) data for underlying investments is as of the prior period, as reported by underlying managers up to the business day occurring on or after 100 days, or 115 days at the fiscal year-end, following the prior period end. When NAV data is not available by the business day occurring on or after 100 days, or 115 days at the fiscal year-end, following the prior period end, such NAVs are adjusted for cash activity following the last available reported NAV.
    (1) Allocation of AUM by asset class is presented by underlying investment asset classification.
    (2) Total capital responsibility equals assets under management (AUM) plus assets under advisement (AUA).    

    Contacts

    Shareholder Relations:
    Seth Weiss
    shareholders@stepstonegroup.com
    1-212-351-6106

    Media:
    Brian Ruby / Chris Gillick / Matt Lettiero, ICR
    StepStonePR@icrinc.com
    1-203-682-8268

    Glossary

    Assets under advisement, or “AUA,” consists of client assets for which we do not have full discretion to make investment decisions but play a role in advising the client or monitoring their investments. We generally earn revenue for advisory-related services on a contractual fixed fee basis. Advisory-related services include asset allocation, strategic planning, development of investment policies and guidelines, screening and recommending investments, legal negotiations, monitoring and reporting on investments, and investment manager review and due diligence. Advisory fees vary by client based on the scope of services, investment activity and other factors. Most of our advisory fees are fixed, and therefore, increases or decreases in AUA do not necessarily lead to proportionate changes in revenue. We believe AUA is a useful metric for assessing the relative size of our advisory business.

    Our AUA is calculated as the sum of (i) the NAV of client portfolio assets for which we do not have full discretion and (ii) the unfunded commitments of clients to the underlying investments. Our AUA reflects the investment valuations in respect of the underlying investments of our client accounts on a three-month lag, adjusted for new client account activity through the period end. Our AUA does not include post-period investment valuation or cash activity. AUA as of March 31, 2025 reflects final data for the prior period (December 31, 2024), adjusted for net new client account activity through March 31, 2025. NAV data for underlying investments is as of December 31, 2024, as reported by underlying managers up to the business day occurring on or after 115 days following December 31, 2024. When NAV data is not available by the business day occurring on or after 115 days following December 31, 2024, such NAVs are adjusted for cash activity following the last available reported NAV.

    Assets under management, or “AUM,” primarily reflects the assets associated with our separately managed accounts (“SMAs”) and focused commingled funds. We classify assets as AUM if we have full discretion over the investment decisions in an account or have responsibility or custody of assets. Although management fees are based on a variety of factors and are not linearly correlated with AUM, we believe AUM is a useful metric for assessing the relative size and scope of our asset management business.

    Our AUM is calculated as the sum of (i) the net asset value (“NAV”) of client portfolio assets, including the StepStone Funds and (ii) the unfunded commitments of clients to the underlying investments and the StepStone Funds. Our AUM reflects the investment valuations in respect of the underlying investments of our funds and accounts on a three-month lag, adjusted for new client account activity through the period end. Our AUM does not include post-period investment valuation or cash activity. AUM as of March 31, 2025 reflects final data for the prior period (December 31, 2024), adjusted for net new client account activity through March 31, 2025. NAV data for underlying investments is as of December 31, 2024, as reported by underlying managers up to the business day occurring on or after 115 days following December 31, 2024. When NAV data is not available by the business day occurring on or after 115 days following December 31, 2024, such NAVs are adjusted for cash activity following the last available reported NAV.

    Consolidated Funds refer to the StepStone Funds that we are required to consolidate as of the applicable reporting period. We consolidate funds and other entities in which we hold a controlling financial interest.

    Consolidated VIEs refer to the variable interest entities that we are required to consolidate as of the applicable reporting period. We consolidate VIEs in which we hold a controlling financial interest.

    Fee-earning AUM, or “FEAUM,” reflects the assets from which we earn management fee revenue (i.e., fee basis) and includes assets in our SMAs, focused commingled funds and assets held directly by our clients for which we have fiduciary oversight and are paid fees as the manager of the assets. Our SMAs and focused commingled funds typically pay management fees based on capital commitments, net invested capital and, in certain cases, NAV, depending on the fee terms. Management fees are only marginally affected by market appreciation or depreciation because substantially all of the StepStone Funds pay management fees based on capital commitments or net invested capital. As a result, management fees and FEAUM are not materially affected by changes in market value. We believe FEAUM is a useful metric in order to assess assets forming the basis of our management fee revenue.

    Legacy Greenspring entities refers to certain entities for which the Company, indirectly through its subsidiaries, became the sole and/or managing member in connection with the Greenspring acquisition.

    SSG refers solely to StepStone Group Inc., a Delaware corporation, and not to any of its subsidiaries.

    StepStone Funds refer to SMAs and focused commingled funds of the Company, including acquired Greenspring funds, for which the Partnership or one of its subsidiaries acts as both investment adviser and general partner or managing member.

    The Partnership refers solely to StepStone Group LP, a Delaware limited partnership, and not to any of its subsidiaries.

    Total capital responsibility equals AUM plus AUA. AUM includes any accounts for which StepStone Group has full discretion over the investment decisions, has responsibility to arrange or effectuate transactions, or has custody of assets. AUA refers to accounts for which StepStone Group provides advice or consultation but for which the firm does not have discretionary authority, responsibility to arrange or effectuate transactions, or custody of assets.

    Undeployed fee-earning capital represents the amount of capital commitments to StepStone Funds that has not yet been invested or considered active but will generate management fee revenue once invested or activated. We believe undeployed fee-earning capital is a useful metric for measuring the amount of capital that we can put to work in the future and thus earn management fee revenue thereon.

    The MIL Network

  • MIL-OSI USA: Tillis Introduces Legislation to Target Predatory Litigation Funding Practices

    US Senate News:

    Source: United States Senator for North Carolina Thom Tillis
    WASHINGTON, D.C. – This week, Senator Thom Tillis introduced the Tackling Predatory Litigation Funding Act, legislation which would impose a new tax on profits earned by third-party entities that finance civil litigation and curb predatory practices in the litigation funding industry.
    “Predatory litigation financing allows outside funders, including foreign entities, to profit off our legal system, driving up costs and delaying justice,” said Senator Tillis. “This legislation will bring much-needed transparency and accountability by taxing these profits and deterring abusive practices that undermine the integrity of our courts.”
    Representative Kevin Hern (R-OK) introduced companion legislation in the House of Representatives.
    “Foreign entities shouldn’t be allowed to meddle tax-free in the American legal system. Frivolous lawsuits have gotten out of control in recent years, largely because of these third-party funders fueling a market that is ballooning,” said Representative Hern. “Taxing these third-party entities will limit unmeritorious lawsuits and provide economic relief to the middle class.”
    Background:
    Third-party litigation funding (TPLF) is the practice of an outside party to a legal dispute paying for a lawsuit with the expectation of financially profiting off the outcome. This highly questionable practice adds tremendous costs to U.S. consumers by encouraging and needlessly extending litigation. It is also arguably violative of several common law principles that seek to prevent profit-seeking and abusive practices in the tort system. 
    The involvement of otherwise uninterested parties gambling on the outcome of litigation also raises significant concerns that this funding disrupts the attorney-client relationship. This practice remains hidden in the shadows, as there is no comprehensive disclosure regime for when a TPLF contract exists for a lawsuit. Despite this lack of disclosure, TPLF market participants acknowledge that the litigation funding industry has exploded over the last decade, with the largest year-over-year growth in capital commitments reported in 2022. 
    There is now estimated to be well over $15 billion deployed for U.S. litigation financing, with the leading firm seeing a 355% increase in its assets over the last several years, including the addition of nearly $1 billion at the end of 2018 by an unknown, foreign sovereign wealth fund.
    While these TPLF investment firms are treating the U.S. court system like a casino, there are real questions about the tax treatment of the financial returns from litigation funding. By structuring TPLF contracts as complex investment vehicles, funders pay a more favorable tax rate on their share of a court award when compared to the actual injured plaintiff – while in many cases receiving more total money than the injured party.
    With capital gains treatment, foreign investors can create a situation in which they avoid any U.S. tax obligation on their returns despite using the U.S. court system to generate profit. Perversely, this incentivizes foreign investment in more U.S. litigation because of the potential for lucrative, tax-free returns. The current situation is unfair and untenable and the time has come for lawmakers to update current tax law to address these issues.
    The following organizations support the Tackling Predatory Litigation Funding Act:American Consumer Institute, 60 Plus Association, Advancing American Freedom, American Association of Senior Citizens, Americans for Tax Reform, Center for Individual Freedom, Citizens Against Lawsuit Abuse, Consumer Action for a Strong Economy, Consumer Choice Center, Council for National Policy Action, Frontiers of Freedom, Heartland Impact, Institute for Liberty, Less Government, National Taxpayers Union, Taxpayers Protection Alliance, Heartland Institute, and the James Madison Institute.  
    Full text of the bill is available HERE.

    MIL OSI USA News

  • MIL-OSI Security: Hollywood Man Sentenced to Nearly 5 Years in Prison for Fraudulently Seeking Millions of Dollars in COVID Tax Breaks

    Source: Office of United States Attorneys

    LOS ANGELES – A Hollywood man who admitted to seeking more than $65 million from the IRS by falsely claiming on tax returns that his nonexistent farming business was entitled to COVID-19-related tax credits was sentenced today to 57 months in federal prison.

    Kevin J. Gregory, 57, was sentenced by United States District Judge Josephine L. Staton, who also ordered him to pay $2,769,173 in restitution.

    Gregory, who has been in federal custody since May 2023, pleaded guilty on January 17 to one count of making false claims to the IRS.

    In response to the COVID-19 pandemic and its economic impact, Congress authorized an employee retention tax credit that a small business could use to reduce the employment tax it owed to the IRS, also known as the “employee retention credit.”

    To qualify, the business had to have been in operation in 2020 and to have experienced at least a partial suspension of its operations because of a government order related to COVID-19 (for example, an order limiting commerce, group meetings or travel) or a significant decline in profits. The credit was an amount equal to a set percentage of the wages that the business paid to its employees during the relevant time period, subject to a maximum amount.

    Congress also authorized the IRS to give a credit against employment taxes to reimburse businesses for the wages paid to employees who were on sick or family leave and could not work because of COVID-19. This “paid sick and family leave credit” was equal to the wages the business paid the employees during the sick or family leave, also subject to a maximum amount.

    From November 2020 to April 2022, Gregory made false claims to the IRS for the payment of nearly $65.3 million in tax refunds for a purported Beverly Hills-based farming-and-transportation company named Elijah USA Farm Holdings.

    The IRS issued a portion of the refunds Gregory claimed, and Gregory used a significant portion – more than $2.7 million – for personal expenses.

    Specifically, in January 2022, Gregory made a false claim to the IRS for the payment of a tax refund in the amount of $23,877,620, which he submitted as part of Elijah Farm’s quarterly federal tax return. Gregory claimed Elijah Farm employed 33 people, paid nearly $1.6 million in quarterly wages, had deposited nearly $18 million in federal taxes, and was entitled to nearly $6.5 million in COVID-relief tax credits.

    In fact, Gregory knew that Elijah Farm employed nobody and paid wages to no one and had not made federal tax deposits to the IRS in the amounts stated on his tax return.

    IRS Criminal Investigation investigated this matter.

    Assistant United States Attorney Kristen A. Williams of the Major Frauds Section prosecuted this case.

    On May 17, 2021, the Attorney General established the COVID-19 Fraud Enforcement Task Force to marshal the resources of the Department of Justice in partnership with agencies across government to enhance efforts to combat and prevent pandemic-related fraud. The Task Force bolsters efforts to investigate and prosecute the most culpable domestic and international criminal actors and assists agencies tasked with administering relief programs to prevent fraud by, among other methods, augmenting and incorporating existing coordination mechanisms, identifying resources and techniques to uncover fraudulent actors and their schemes, and sharing and harnessing information and insights gained from prior enforcement efforts. More information on the Justice Department’s response to the pandemic may be found here

    Anyone with information about allegations of attempted fraud involving COVID-19 can report it to the Department of Justice’s National Center for Disaster Fraud (NCDF) Hotline at (866) 720-5721 or via the NCDF online complaint form.

    MIL Security OSI

  • MIL-OSI USA: May 22nd, 2025 Heinrich, Luján Introduce Legislation to Expand Medicare Drug Price Negotiation and Lower Costs for New Mexicans

    US Senate News:

    Source: United States Senator for New Mexico Martin Heinrich

    WASHGINTON — U.S. Senators Martin Heinrich (D-N.M.) and Ben Ray Luján (D-N.M.) introduced the Strengthening Medicare and Reducing Taxpayer (SMART) Prices Act, legislation that will expand Medicare negotiation of drug prices to lower drug costs for consumers, reduce federal spending, and give the U.S. Department of Health and Human Services (HHS) stronger tools to negotiate lower drug prices in Medicare Part B and Part D.

    According to preliminary estimates from a model by West Health and Verdant Research, if the SMART Prices Act is enacted by 2026, it would save 33 percent more by 2030 than current law. It would also allow Medicare to begin negotiations earlier and bring down the price of more expensive drugs.

    The legislation builds on provisions passed into law by Heinrich and Luján in 2022 that empowered Medicare to negotiate prescription drug prices for the first time. The SMART Prices Act extends this progress by more than doubling the number of prescription drugs Medicare must negotiate to a minimum of 50 per year, allowing the most costly prescription drugs and biologics to have negotiated prices five years after approval by the Food and Drug Administration, and by increasing the discount that Medicare is allowed to negotiate.

    “While the Trump Administration and Congressional Republicans work to gut Medicare to give massive tax handouts to billionaires like Elon Musk, I’m fighting to protect and strengthen Medicare for New Mexicans,” said Heinrich. “I’m proud to co-sponsor legislation that will lower health care costs by making more prescription drugs affordable for New Mexico’s seniors enrolled in Medicare.”

    “No one should have to choose between paying for life-saving medication and putting food on the table. At a time when President Trump’s tariffs threaten to raise prices on everyday goods and medicine, the SMART Prices Act is more important than ever for New Mexican families,” said Luján. “That’s why I’m proud to join my colleagues in introducing this legislation to lower prescription drug costs by strengthening Medicare’s ability to negotiate prices, helping Americans afford the medications they rely on.”

    The SMART Prices Act is led by U.S. Senators Amy Klobuchar (D-Minn.) and Peter Welch (D-Vt.). Alongside Heinrich and Luján, the legislation is co-sponsored by U.S. Senators Tammy Baldwin (D-Wis.), Michael Bennet (D-Colo.), Richard Blumenthal (D-Conn.), Cory Booker (D-N.J.), Maria Cantwell (D-Wash.), Catherine Cortez Masto (D-Nev.), Tammy Duckworth (D-Ill.), Dick Durbin (D-Ill.), John Fetterman (D-Pa.), Kirsten Gillibrand (D-N.Y.), Maggie Hassan (D-N.H.), Angus King (I-Maine), Ed Markey (D-Mass.), Jeff Merkley (D-Ore.), Chris Murphy (D-Conn.), Patty Murray (D-Wash.), Jack Reed (D-R.I.), Jeanne Shaheen (D-N.H.), Elissa Slotkin (D-Minn.), Tina Smith (D-Minn.), Chris Van Hollen (D-Md.), Elizabeth Warren (D-Mass.), and Sheldon Whitehouse (D-R.I.).

    The bill is endorsed by Center for American Progress, FamiliesUSA, Patients For Affordable Drugs NOW, Protect Our Care, and Public Citizen.

    As Republicans tank the economy, Heinrich and Luján are putting New Mexico families first and fighting against Trump and Musk’s budget, which includes cuts to Medicaid to fund massive tax handouts to billionaires.

    Earlier this month, Heinrich and Luján (D-N.M.) released a joint statement slamming President Trump’s Fiscal Year 2026 (FY26) preliminary budget request. In their joint statement, the senators wrote, “Donald Trump and Elon Musk’s budget will further tank the economy and throw working families under the bus. As New Mexico’s senators, we’ll fight back.”

    Last month, Heinrich and Luján stood up for New Mexico families by voting against Senate Republicans’ budget resolution. This was after Heinrich and Luján pushed to amend Republicans’ resolution by repeatedly voting for amendments to lower costs for families — particularly as Trump’s tariffs push America to the brink of a recession. Heinrich and Luján also worked to block cuts to Medicaid, extend the tax credits for health care premiums, and prevent millions of Americans from losing health insurance, protect Social Security, and reverse cuts to the Social Security Administration, including cuts by Elon Musk’s DOGE.

    MIL OSI USA News