agent-security

To secure AI agent architecture, review permission models, enforce least-privilege, map multi-agent trust boundaries, and assess human-in-the-loop oversight. Structured findings align with OWASP Agentic AI threats and NIST AI RMF 1.0 to ensure blast-radius containment and auditability for tool-using autonomous systems.

Autonomous AI agents require least-privilege enforcement, scoped tool-access, human-in-the-loop gate placement, rollback capability, and rate or budget limits. Forensic-grade audit logging ensures accountability and incident investigation readiness across multi-agent trust boundaries.

Threat modeling maps AI agent risks to OWASP Agentic AI threats and NIST AI RMF 1.0 by evaluating injection-hardened defenses, trust boundaries, and audit trails. This produces structured findings for permission modeling, human oversight, and rollback capability in multi-agent systems.

Yes, assessing multi-agent trust boundaries and blast-radius containment is a core function of agent security review. It evaluates permission models and least-privilege enforcement to prevent insecure architecture deployment and limit the impact of tool-using autonomous actions.

AI agent security architecture reviews are limited to defensive, injection-hardened evaluation of system designs rather than runtime monitoring. Scoped tool-use is restricted to Read, Grep, and Glob operations, meaning it assesses architectural blueprints against OWASP and NIST frameworks without active penetration testing.