The San Juan Unified School District in California accepted over $175,000 from the Council on American-Islamic Relations (CAIR) in fiscal year 2024 — an organization that two U.S. state governors have officially designated as a foreign terrorist organization, and that the FBI suspended all formal contacts with in 2009 over evidence linking its founders to Hamas. Documents obtained by the watchdog group Defending Education reveal extensive financial ties between the 40,000-student district and CAIR, including youth programming inside schools and CAIR’s participation in strategic planning committees — raising urgent questions about institutional oversight, student safety, and the protection of American values in public education.

Deep Partnership Extended Beyond Funding

Emails obtained by Defending Education reveal that CAIR’s involvement with San Juan Unified extended far beyond monetary donations. In March 2024, a CAIR youth coordinator thanked district officials for inviting the organization to “Starr King’s parent night and the SJUSD Strategic Core planning,” noting that CAIR had “successfully launched” a six-week Leadership Development program at Starr King, an elementary and middle school in the district.

A June 2024 email confirmed that “youth from the district” were attending CAIR workshops. The partnership dates back to at least 2022, when a memorandum of understanding showed CAIR provided $180,000 to the district — bringing total documented funding to over $355,000 across two fiscal years. CAIR representatives maintained ongoing communication with district staff about grant reporting requirements and discussed expanding programs to additional schools.

Student Privacy Concerns Raised

In May 2024, a CAIR representative told district staff in an email: “Along with your monthly invoice reports you are required to submit the participants served report.” A district employee then asked a colleague, “We are using CAIR to pay for the attached IntelliBricks invoices. How do I get a list of the participants served?”

According to the Washington Examiner, documents indicate that “CAIR requested student names, and district personnel provided them.” The district’s provision of student names to CAIR as part of grant reporting appears to raise concerns under the Family Educational Rights and Privacy Act (FERPA), which prohibits schools from disclosing personally identifiable information from education records to third parties without parental consent.

When Defending Education submitted a public records request seeking documentation about the CAIR funding, the district provided documents containing a list of refugee students with their full names and “home language” — including Arabic, Dari, Farsi, Pashto, Russian, and Ukrainian — completely unredacted.

CAIR Executive Celebrated Hamas Attack

Following Hamas’s October 7, 2023 terrorist attack on Israel that killed over 1,200 people including 46 Americans, CAIR Executive Director Nihad Awad stated at a conference: “I was happy to see people breaking the siege...the people of Gaza have the right to self-defense, have the right to defend themselves, and yes, Israel, as an occupying power, does not have that right to self-defense.” The White House subsequently distanced itself from CAIR following the remarks.

In October 2025, CAIR-Ohio Director Khaled Tuurani participated in an online conference alongside Majed al-Zeer, a Hamas official whom the U.S. Treasury designated as a Specially Designated Terrorist in 2024 for his role in Hamas fundraising efforts. The George Washington University Program on Extremism documented that both appeared on panels for an event whose purpose was to plan strategy “in the Light of Al-Aqsa Flood” — Hamas’s name for the October 7 massacre.

FBI Suspended Contact Over Hamas Evidence

In 2009, the FBI explained to U.S. Senator Jon Kyl that it had “suspended all formal contacts between CAIR and the FBI” due to evidence from the Holy Land Foundation terrorism financing trial linking CAIR and its founders to Hamas. FBI Assistant Director Richard C. Powers wrote that “evidence was introduced that demonstrated a relationship among CAIR, individual CAIR founders...and the Palestine Committee,” which federal prosecutors showed was created by the Muslim Brotherhood to support Hamas politically and financially in the United States.

The FBI stated it could not resume relations “until we can resolve whether there continues to be a connection between CAIR or its executives and Hamas.” That determination has never been made. CAIR founders Omar Ahmad and Nihad Awad — the latter still serving as executive director participated in a secretly recorded 1993 Philadelphia meeting where Hamas members and supporters discussed ways to “derail” U.S.-led peace efforts following the Oslo Accords, according to FBI wiretaps introduced at trial. CAIR was named as an unindicted co-conspirator in the Holy Land Foundation case, the largest terrorism financing prosecution in U.S. history.

Two State Governors Issue Terrorist Designations

Texas Governor Greg Abbott designated both the Muslim Brotherhood and CAIR as foreign terrorist organizations in November 2025, citing the groups’ goals to “forcibly impose Sharia law and establish Islam’s ‘mastership of the world.’” The designation authorizes heightened enforcement and prohibits both organizations from purchasing land in Texas.

Florida Governor Ron DeSantis issued a similar executive order the following month, noting that CAIR “was designated as an unindicted co-conspirator by the United States Government in the largest terrorism-financing case in American history.” The order directed Florida law enforcement to “undertake all lawful measures to prevent unlawful activities in Florida by the terrorist organizations designated.”

In Congress, Representative Randy Fine introduced H.R. 4097, the “Designate CAIR as a Terrorist Organization Act,” which directs the Secretary of State to conduct a formal review of whether CAIR meets federal criteria for designation as a foreign terrorist organization. The United Arab Emirates, a U.S. ally in the Middle East, designated CAIR as a terrorist organization in 2014.

Risks to American Educational Institutions

The San Juan partnership represents a troubling pattern of CAIR embedding itself within American civic institutions despite evidence linking the organization to designated terrorist networks. The district’s leadership allowed an organization that the FBI suspended contact with and that two U.S. governors designated as a terrorist group to operate youth programs inside schools, participate in strategic planning, and potentially access student information without adequate transparency or parental notification.

By granting CAIR involvement with refugee students, many from countries affected by Islamic extremism, and including the organization in district-level planning committees, San Juan Unified prioritized funding over institutional safeguards. The lack of public disclosure about the partnership, combined with concerns about student privacy protections, raises fundamental questions about accountability in American public education and the due diligence required when accepting funds from controversial organizations.

The face in the video looked real, the driver’s license appeared authentic, and the account passed all automated checks. Three weeks later, investigators discovered the customer never existed.

Treasury’s Financial Crimes Enforcement Network issued an alert in November 2024 warning that criminals were using deepfake media to bypass identity verification at U.S. financial institutions. By the time the department’s new report to Congress landed last week, the problem had evolved beyond warnings into documented losses: malicious actors had successfully opened accounts using fraudulent identities suspected to have been produced with generative AI, then used those accounts to launder proceeds from other fraud schemes. According to analysis of Bank Secrecy Act data, this isn’t theoretical risk- it’s operational reality.

I’ve been trying to understand the mechanics of how verification systems failed, and the technical details are both straightforward and unsettling. Criminals create deepfake images by modifying authentic source photos or generating synthetic ones entirely. They combine these AI-generated images with stolen personally identifiable information scraped from data breaches, or fabricate the PII entirely using the same generative models. The resulting synthetic identity includes a face that doesn’t exist, credentials that appear legitimate, and behavioral signals that fool automated screening.

Traditional “Know Your Customer” verification relied on document authenticity: does this driver’s license match government databases, do the security features check out, does the face in the photo match the face in the video call? Generative AI breaks this model because the documents are authentic in every verifiable way except the fundamental fact that the person doesn’t exist. A passport photographed at the correct angle, a utility bill in standard format, a selfie video with natural lighting and movement, all produced by algorithms trained on millions of real examples.

The scale shifts fraud economics dramatically. Deepfake files surged from 500,000 in 2023 to 8 million in 2025, according to industry tracking. Fraud attempts using this technology spiked 3,000% in 2023 alone, with 1,740% growth in North America. Contact centers reported a 680% year-over-year rise in deepfake activity. What previously required skilled forgers or insider access to identity databases now requires consumer-grade AI tools and publicly available training data.

Treasury’s response is to fight automation with automation, which sounds either sophisticated or recursive depending on your perspective. The department recommends financial institutions deploy AI systems capable of analyzing blockchain transaction patterns, simulating money laundering scenarios, and adapting to evolving criminal tactics in real time. Specifically: entity resolution through graph analysis to map connections among wallets and exchanges; behavioral monitoring to detect synthetic attempts through login patterns and device signals; algorithms that identify “chain-hopping” (moving assets across blockchains) and “smurfing” (structuring small deposits across multiple accounts).

The technical approach has a certain logic. AI-powered models can process transaction data at speeds human analysts cannot match, learning sequences that indicate money laundering- deposit to exchange, convert to privacy coin, transfer through mixer, swap to stablecoin, withdraw through different exchange—and flagging those patterns before funds disappear into jurisdictions beyond U.S. reach. Some tools can now interdict fraudulent transactions in real time by identifying when a customer’s digital asset wallet interacts with known scam websites at the moment of transfer.

Large language models offer different capabilities: automating adverse media and sanctions screening checks, synthesizing vast amounts of unstructured data to assist case reviews, even drafting suspicious activity report narratives. Compliance personnel describe faster and deeper analysis than manual review allows. One institution reported using AI to cut Know Your Customer verification costs by 60% over 18 months by handling non-standard documents- passports photographed at angles, utility bills in foreign formats, corporate filings in languages automated systems previously rejected.

The challenge is that criminals have access to the same technology. Treasury’s report notes that generative AI tools are used for phishing campaigns, scanning breached data repositories to extract PII, and creating high-quality fraudulent documents that fool both automated systems and human reviewers. Industry sources report that manual analysts “can no longer tell legitimate from fraudulent with the human eye” when examining AI-generated identity documents. The visual and structural tells that previously flagged forgeries—inconsistent shadows, pixelation artifacts, formatting errors, disappear when algorithms generate documents from the same training data that verification systems use to validate authenticity.

This creates an adversarial cycle: institutions deploy machine learning models to detect synthetic identities, criminals train generative models to fool those detection systems, institutions add liveness detection (verifying that biometric samples come from living persons rather than deepfake videos), criminals develop techniques to defeat liveness checks. Each iteration raises the sophistication floor for both sides.

Financial institutions highlighted to Treasury that data quality and model validation remain barriers to implementation. AI systems operate as “black boxes” that complicate compliance teams’ ability to explain decisions to regulators or customers. Historical transaction data may reflect enforcement biases that skew model outcomes. Upfront costs for adoption prove prohibitive for smaller institutions unable to dedicate resources to training custom systems. Ongoing expenses for model maintenance, governance, and monitoring add to the burden, particularly as cybersecurity risks from AI tools themselves emerge as concerns.

What surprised me in Treasury’s recommendations is the acknowledgment that existing regulatory frameworks, while technically technology-agnostic, weren’t designed with AI capabilities in mind. The department suggests financial institutions align their AI model development with the National Institute of Standards and Technology’s AI Risk Management Framework, which emphasizes transparency, documentation, and model-risk validation. The implicit message: regulators want institutions to stop running legacy rules-based systems in parallel with machine learning models once the AI approach proves effective. Parallel runs increase costs, create inefficiencies between systems, and waste investigatory resources on false positives.

The geopolitical dimension complicates this further. The report describes AI-powered entity resolution mapping connections across “multi-jurisdictional networks that may evade detection by legacy, rules-based systems.” North Korean hackers, for instance, have proven adept at using complex laundering sequences that exploit the seams between different regulatory regimes and technology platforms. AI detection systems need to track activity across blockchains, through mixers, across bridges, and into over-the-counter brokers who prefer stablecoins, each step potentially involving different jurisdictions with varying levels of cooperation.

Treasury estimates consumers lost $12.5 billion to fraud in 2024, with digital asset-related fraud accounting for $9 billion of that total. Investment scams using AI-enhanced social engineering—the “pig butchering” schemes run from industrial-scale operations in Southeast Asia—netted $5.8 billion alone. These aren’t failures of technology but of the adversarial balance between defensive and offensive capabilities.

The deeper question is whether detection can keep pace with generation. Machine learning models improve through training on larger datasets; so do generative models producing synthetic identities. Financial institutions invest in graph analysis to map wallet connections; criminals fragment transactions across more wallets and exchanges. Banks deploy liveness detection for video verification; deepfake tools advance to defeat those checks. The technical capabilities are symmetric, which means advantage comes down to resources, speed of implementation, and willingness to accept false positives versus false negatives in screening.

Treasury’s policy recommendations suggest betting on scale: public-private partnerships to share best practices, guidance encouraging AI adoption for compliance, alignment with NIST frameworks to provide regulatory clarity. The logic is that financial institutions collectively have more resources and data than criminals, making sophisticated AI detection economically viable where human review isn’t. But the report also notes that institutions worry about regulatory uncertainty in deploying systems whose decision-making processes they cannot fully explain.

This is the paradox of adversarial AI in financial crime: the same opacity that makes these systems powerful—their ability to identify patterns humans miss, to process data at speeds manual review cannot match—also makes them difficult to audit, explain, or trust. When an algorithm flags a transaction as suspicious based on a complex web of behavioral signals and network connections, compliance teams face the challenge of translating that into actionable intelligence or defensible regulatory reports.

The result is an arms race with no obvious equilibrium. Better detection drives criminals toward more sophisticated generation. More sophisticated generation forces institutions to deploy more complex detection. Each side’s advances push the other toward greater technical capability and higher operational costs. The question isn’t whether AI will be used for both fraud and fraud detection—that ship has sailed. It’s whether defensive applications can maintain parity with offensive ones, and at what cost to institutions, regulators, and the customers caught in between.

Treasury’s report amounts to an acknowledgment that there’s no going back to manual verification and rules-based screening. The technology exists, criminals are using it, and the only question is how quickly financial institutions can build detection capabilities sophisticated enough to keep up. Whether that constitutes progress or just an escalating cycle of technical complexity is a matter of perspective.

As the Federal Reserve concludes its March 18, 2026, meeting with a widely expected rate hold, the underlying economic data reveals a constraint that interest rates cannot easily fix: a deepening “Brain Trust Cliff.” While financial markets focus on stagflation and oil shocks from the Iran conflict, American institutions are facing the physical exit of their most experienced workers—and finding that their digital replacements are not yet ready to take the wheel.

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The Exit of the Experts

The reality of the “Silver Tsunami” has shifted from a demographic forecast to an operational crisis. As of January 2026, the labor force participation rate for workers aged 55 and older has stabilized around 37.9%, but the concentration of these workers in critical infrastructure is perilously high.

In the Utilities sector, for example, 80% of all employment is now at firms where at least a quarter of the workforce is over age 55—up from just 35% in 2006. When these individuals retire, they aren’t just leaving a vacancy; they are taking the “unwritten rules” of the grid with them. This isn’t a simple hiring problem—it is an institutional memory leak that threatens the stability of foundational U.S. services.

The AI Implementation Gap

To bridge this gap, 88% of organizations have deployed AI as of early 2026. The goal is to “capture” expert knowledge into agentic systems that can guide the next generation. However, the 2026 Deloitte Global Human Capital Trends report reveals a stark “readiness gap”: executives are struggling to manage the human-AI interaction effectively.

Instead of a smooth transition, many firms are accumulating “culture debt”—the friction caused when complex, human-centered processes are automated without regard for the social fabric of the workplace. This has led to a “blind flying” period: the experts are gone, the AI is generating errors, and younger workers are spending significant time reworking poor AI-generated content. According to Workday’s 2026 research, while 85% of employees report saving one to seven hours per week using AI, nearly 40% of those time savings are lost to correcting errors, rewriting content, and verifying outputs.

The “Leapfrog” of Generation X

The most significant undercovered detail of this transition is the “leapfrog effect” on Generation X. Often called the “bridge generation” for their comfort with both analog and digital eras, Gen Xers are being overlooked for leadership roles even as they are expected to manage the crisis.

The 2026 Mather Institute Gen Xperience report found that while Gen X now holds a smaller percentage of executive-level positions compared to millennials and even boomers in some cases, they are the most likely to stay with their employers for 10+ years—with 38% intending to remain that long. By “leapfrogging” these stabilizers in favor of younger “AI-native” talent or aging-in-place Boomers, companies are accidentally destroying the very bridge they need to transfer institutional knowledge safely.

In the coming months, the public will be watching for a shift in corporate spending from “AI tools” to “human-in-the-loop” mentorship programs as firms realize that software cannot replace context. Additionally, there may be specialized recruitment drives targeting the “unretired”—Boomers brought back as consultants specifically to train AI agents and Gen X leaders. The success of the 2026 economy may depend less on how much society automates, and more on how well it protects the knowledge transfer.