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Federal agencies now face a major shift in how they view “insiders.” In 2026, AI systems themselves have become insiders, handling sensitive tasks at machine speed. This change is pushing agencies to rethink their approach to insider risk. Back in January, the Cybersecurity and Infrastructure Security Agency (CISA) issued updated guidance on insider threats, calling on critical infrastructure to take action. For federal agencies, the threat now goes beyond people acting with bad intent. It also includes misconfigured AI systems, fabricated identities, and seemingly harmless behaviors that quietly enable data theft. The White House’s newly released Cyber Strategy also highlights the urgency to protect critical infrastructure and stresses the need to transform how we safeguard our overall cybersecurity.
The definition of “insider” now goes beyond humans
AI systems now carry out sensitive, mission-critical work once done only by cleared human employees. But they bring a new kind of risk that moves much faster than traditional human-focused controls. Organizations like the National Institute of Standards and Technology (NIST) and the Office of Management and Budget (OMB) have specifically warned that a misconfigured or tampered-with AI system can cause damage in milliseconds. This makes behavioral monitoring and AI governance an urgent priority.
Since these autonomous systems operate with significant authority but lack the oversight traditionally applied to people, they create a huge regulatory gap. This shift means a compromised or malicious AI can slip past standard identity governance and security controls built for human federal workers. Without a fundamental overhaul of these frameworks, agencies risk losing operational control entirely, as these digital insiders can carry out complex, unauthorized actions before a human even spots something wrong.
Trust and identity in federal agencies are breaking down at scale
Federal agencies are overwhelmed by a twin threat: AI-powered fraud and an explosion of non-human identities. Deepfake impersonation and AI-driven social engineering are advancing rapidly, making it far more likely that federal staff will be tricked into giving up sensitive data or access. CISA and FBI have issued recent warnings about sophisticated synthetic identity fraud and impersonation campaigns that mimic senior officials. As cybersecurity and law enforcement experts have put it, “Current defenses don’t offer strong enough protection against these risks. We’re pushing the community to develop better solutions.”
This breakdown in trust is made worse by “identity sprawl,” where non-human identities including bots, service accounts, and AI agents now outnumber human staff by more than 20 to 1. These machine-level accounts often have high-level access but lack the monitoring needed to detect if they’ve been compromised. The Government Accountability Office (GAO) keeps flagging these machine identities as poorly governed and rarely audited across the federal enterprise. Unless agencies change how they manage this huge non-human workforce, their systems remain vulnerable to “silent” insiders that attackers can exploit to bypass traditional security controls built around human users.
People are still at the heart of insider risk
Even with the rapid growth of autonomous technology, the main causes of insider incidents are still human. Federal workers dealing with constant fatigue, distractions, and mission pressure are more likely to make security mistakes. In fact, 74% of chief information security officers (CISOs) surveyed by Proofpoint pointed to human error as their biggest cybersecurity risk, showing that technical protections are only as reliable as the people managing them. But AI is dramatically changing the insider threat landscape. It’s no longer just about accidental mistakes. Now there’s a risk of AI agents colluding with each other at speeds that were never possible before. Past exploits involving OpenClaw illustrate this shift, showing how malicious processes can work together to get around traditional defenses. If a malicious insider with political or nation-state motives uses these AI-driven methods, the consequences could be severe.
On top of that, human error gets worse because of “privilege creep” in federal identity, credential, and access management (ICAM) programs, where employees gather too many permissions over time. These over-privileged accounts dramatically increase the impact of any insider incident, turning a small mistake into a widespread failure. Since humans still set up, approve, and oversee AI and machine identities across federal networks, behavioral risk remains an unavoidable issue that demands ongoing, proactive monitoring.
Organizations need to treat both corporate and agentic AI as high-risk systems. That means enforcing approved use cases, applying least-privilege access, using layered security controls, running adversarial testing, and maintaining strong governance to prevent data leaks, misuse, and new AI-driven threats. As agentic AI tools take on more tasks independently, standards like the CIS Controls, NIST SP 800-53, and ISO/IEC 27001 reinforce the need to limit scripting and command-line access to only essential users. Organizations must also monitor prompts, autonomous actions, and behavioral patterns to spot insider risks, support investigations, and stay compliant as AI autonomy grows.
Closing the insider risk gap
In 2026, insider risk in the federal government isn’t just about watching human insiders. It’s about securing the entire ecosystem that runs federal operations: the people making decisions under pressure, the AI systems executing tasks at scale, and the machine identities running quietly in the background. Agencies that don’t update their insider risk strategies risk losing more than just data. They risk losing trust, resilience, and operational control.
Federal organizations must quickly separate machine identities from human ones across their systems, understand which identities can access and manage sensitive data, and treat both corporate and agentic AI as insider risks. Tightening access controls now is critical. The next major cyber incident could hit the federal sector without any warning. Reducing insider-driven exposure is one risk we can still get ahead of.
Michael Rider is a senior solutions engineer, federal, at DTEX.
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