How do I map the OWASP Agentic AI Top 10 to specific architectural defenses for my AI agents?

Architectural defenses for AI agents should focus on runtime controls, human oversight, and robust identity and authorization mechanisms to address the OWASP Agentic AI Top 10 risks. These defenses should be implemented with a defense-in-depth strategy, ensuring complete mediation and continuous monitoring across all layers of the agentic system.

• Implement a comprehensive runtime enforcement layer that acts as a last line of defense, verifying each step against policy and intent. This includes an LLM gateway (AI proxy/firewall) for all model invocations to enforce authentication, content policies, PII redaction, and rate-limiting. A complementary tool broker should validate tool calls against the agent's identity, active intent, and policy, enforcing Intent-Based Access Control (IBAC). This addresses risks like ASI01 (Agent Goal Hijack) by enforcing strict separation between trusted system definitions and untrusted content, and ASI02 (Tool Misuse and Exploitation) by validating tool arguments and applying least privilege.
• Utilize sandboxing for code execution and untrusted data processing. Any tool that executes generated code or processes untrusted data should operate within properly isolated containers with strict resource limits, no outbound network access except through the broker, ephemeral filesystems, and no access to the agent’s credentials. This directly mitigates ASI05 (Unexpected Code Execution (RCE)) by treating all agent-generated code as untrusted and running it in a restricted sandbox.
• Establish robust identity and authorization management for agents, issuing short-lived, task-scoped tokens bound to a verified per-agent identity. Authorization checks should be continuous, not just at session start, and every action should be validated against a defined purpose or intent. This addresses ASI03 (Agent Identity & Privilege Abuse) by preventing attribution gaps and privilege escalation paths.
• Implement human oversight and override mechanisms for high-stakes or ambiguous decisions. This includes pre-action approval gates for critical actions and post-action review queues for sampled actions, especially those with high risk or anomalous patterns. This helps mitigate ASI09 (Human-Agent Trust Exploitation) by requiring independent, multi-step verification for risky actions.
• Ensure secure inter-agent communication by mutually authenticating and encrypting all channels, and signing/integrity-hashing messages with replay protection. This directly addresses ASI07 (Insecure Inter-Agent Communication).
• Protect against memory and context poisoning by validating and sanitizing every write before it enters memory, and tagging entries with provenance and trust to reject low-trust sources from durable memory. This mitigates ASI06 (Memory & Context Poisoning).