Federal agencies are rapidly moving from evaluating artificial intelligence to deploying it across mission systems, analytics platforms, cybersecurity operations, and citizen services. OMB Memorandum M 25 21 and the Federal AI Strategy and Action Plan reinforce this trajectory, directing agencies to accelerate AI adoption while strengthening governance, accountability, and risk management.
Unfortunately, this acceleration has exposed a structural security gap.
Most federal security approaches were designed to assess infrastructure, identity, cloud configuration, and application security, primarily based on predictable deterministic patterns. However, AI introduces new components and non-deterministic workflows that traditional cybersecurity implementations were not explicitly designed to address such as models, training and retrieval data, autonomous agents, agent-to-agent interactions, and dynamic prompt driven workflows.
As agencies scale pilots into production, they must adopt more intelligent Zero Trust capabilities into the AI layer itself. AI platforms must have intelligent security posture management with continuous monitoring. Content secured with intent-based poslicies. Agentic red teams must rapidly find weaknesses and automatically update policies to mitigate before nefarious actors compromise critical infrastructure. Agent actions must be observed, understood and governed similar to human activity, but at a more rapid pace.
