Enterprise AI is shifting from simple assistants to autonomous agents that reason, invoke tools, and complete multi-step workflows with minimal human input. Unlike traditional software, these agents dynamically determine how to achieve objectives, which introduces new security risks beyond authentication and prompt injection.
Once authenticated, AI agents can deviate from intended goals, overuse tools, or act on manipulated context without traditional security controls detecting the behavior. Runtime trust addresses this gap by continuously validating agent actions against organizational policies during execution, rather than assuming trust based solely on initial authentication.
A practical approach includes intent validation, behavioral monitoring, policy enforcement, least-privilege execution, and human oversight for high-risk decisions. Extending existing zero-trust frameworks with these capabilities can reduce operational risk as AI agents become more autonomous and interconnected.
