Zero Trust for AI Agents Starts With Fixing Zero Visibility

The way we talk about AI agents is shifting, and the way we implement them requires an even more fundamental shift. While earlier discourse focused on how quickly organizations could stand up agents and how much productivity they could promise, a string of recent incidents, including a widely discussed intrusion at Hugging Face during an evaluation of OpenAI agents, has spurred organizations to examine whether speed has outpaced the ability to secure what gets deployed. Security teams are increasingly asking what an agent can reach once it's running, and whether anyone would notice before it mattered. However tempting it may be to jump directly into enforcement controls and detections, you need to look before you leap.

Research from Veeam reveals that 70% of organizations admit that AI workflows are already in contact with sensitive corporate data without full oversight in place, and 67% report that IT cannot fully track the autonomous workflows that employees are building. Shadow AI is just one of the challenges to visibility of AI agents, but it exemplifies how quickly and pervasively this fundamental first step can slip through your grasp.

Zero Trust principles can support an AI governance program, but only in the right order. "You cannot govern what you cannot see" is the underlying principle right at the top of the SANS cheat sheet, Zero Trust for AI Agents: The Security Checklist. The cheat sheet places inventory ahead of every enforcement control and treats this as a foundational prerequisite for a reason. In practice, organizations may skip to a policy enforcement point or an authorization scheme for an agent that has no named owner, no defined scope, and no entry in any inventory. That order of operations is a diagnosis of where Zero Trust programs can fail. A proxy or an authorization layer sitting in front of an unknown population of agents has nothing real to enforce against.