Secure AI agents with Policy in Amazon Bedrock AgentCore

This article explains how to secure AI agents in regulated industries using Policy in Amazon Bedrock AgentCore, with a healthcare appointment scheduling example.

• AI agents need external policy enforcement independent of agent reasoning to prevent security risks
• Cedar language provides deterministic, auditable authorization policies with default-deny semantics
• Policy in AgentCore intercepts every agent-to-tool request through gateways before execution
• Policies can be authored in natural language, forms, or directly as Cedar code
• Identity-based policies ensure patients access only their own records
• Read/write separation controls restrict write operations while allowing broad reads
• Forbid rules hard-stop dangerous patterns like scheduling outside permitted hours
• Policy enforcement is deterministic and independent of LLM reasoning or prompt injection
• Sample healthcare agent available on GitHub with complete implementation and testing examples

Policy in Amazon Bedrock AgentCore provides deterministic, auditable security boundaries for production AI agents by enforcing policies at the gateway layer, separate from agent logic.