AI Agent Access Control
AI agent access control is the enforcement of authorization policies on actions taken by AI agents. It determines, in real time, whether a specific agent is allowed to perform a specific action on a specific resource, based on the identity of the delegating user, the properties of the action, and the policies of the organization.
Why It Matters
Traditional access control systems evaluate permissions at authentication time: a user logs in, receives a role, and that role determines what they can do until the session ends. AI agents require a different model because their actions are non-deterministic. Two identical prompts can produce different tool call sequences.
AI agent access control evaluates each action independently and in context. A read operation on a low-sensitivity resource might be auto-approved. A write operation on a production database in the same session might require human approval. A destructive operation might be denied outright. The policy engine makes these decisions in real time.
This per-action enforcement model is what buyers in regulated industries require. Standing approvals are insufficient because they cannot account for the compositional risk that emerges from an agent's accumulated access path.