AI Agent Security in Production

AI agents expand the application surface area. They interpret untrusted input, pull in external context, and often call tools that can read or change data. That combination means security reviews should look beyond prompt wording and cover the full execution path.

A strong security posture starts with least privilege, clear boundaries between instructions and retrieved content, output validation, and auditability for every sensitive action.

Prompt injection

Agents need defenses against both direct prompt injection and indirect prompt injection hidden inside retrieved content. Sanitization, boundary markers, content trust scoring, and output checks should all work together.

Tool misuse

Every tool should validate parameters, log usage, and enforce authorization outside the model. The application, not the prompt, should decide what is allowed.

Data exposure

Responses should be scanned for sensitive data before delivery. Retrieval should bring back only the minimum context required for the task, and multi-tenant systems should keep histories and indexes isolated.

Operational controls

Rate limits, cost budgets, loop guards, and timeout policies help contain abuse and accidental runaway behavior. These are security controls as much as reliability controls.

Supply chain and dependencies

Framework plugins, model files, and tool wrappers should be treated like any other production dependency. Review them, pin versions, and watch for unusual network or file access during runtime.