Audit AI agent actions with action-level logging that records what the agent was asked, what it decided, which tools and systems it touched, which credential it used, what it proposed, who approved it, and what changed. This produces one traceable record per business action rather than fragments scattered across systems. Mapping those logs to the NIST AI RMF or a MITRE ATLAS technique supports both incident response and compliance. Logging that captures decisions and credential use, not just model outputs, is what makes an agent's behavior defensible after an incident.

Model Context Protocol is usable in the enterprise, but tool discovery must be treated as untrusted ingress. A tool's description, and any later change to it, can carry instructions the model will follow, so tools cannot be trusted on name alone. Public U.S. National Security Agency guidance on MCP recommends inspecting every tool schema before it reaches the model. In practice, run that inspection and exposure management inside a security control plane, pin and review tool definitions, and give agents least-privilege access to only the tools a task requires.

AI security protects models and their outputs, while AI agent security protects the actions an agent takes across enterprise systems. Traditional AI security covers model robustness, data poisoning, bias, and content safety. Agent security adds identity, least-privilege authorization, credential and token handling, real-time policy enforcement, human approval, and action-level audit, because an agent can read, write, and transact rather than only generate text. The distinction matters as agents spread: Gartner projects 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025.