Secure AI agents with Policy in Amazon Bedrock AgentCore

2026-03-12 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Cloud Computing & IT Infrastructure · Depth: Advanced, long

Summary

Amazon Bedrock AgentCore introduces a new Policy feature designed to enhance the safety and security of AI agents, particularly in regulated industries like healthcare. This feature provides a deterministic enforcement layer that operates independently of an agent's reasoning, intercepting and evaluating every agent-to-tool request at runtime via the AgentCore Gateway. It utilizes Cedar, a machine-efficient and human-auditable authorization language, to define fine-grained, identity-aware controls and business rules. Policies can be authored directly in Cedar or generated from natural language descriptions, then validated and analyzed for potential issues before enforcement. The system employs a default-deny posture and a "forbid wins over permit" semantic, ensuring that even if an agent is compromised, external policies prevent unauthorized actions like data exfiltration or unintended transactions. A healthcare appointment scheduling agent example demonstrates how Policy in AgentCore secures patient data and runtime operations.

Key takeaway

For AI Engineers deploying agents in regulated environments, Policy in Amazon Bedrock AgentCore offers a critical security layer. You should integrate external Cedar policies via AgentCore Gateway to enforce deterministic boundaries, protecting sensitive data and preventing prompt injection attacks, regardless of agent behavior. This approach allows you to focus on agent capabilities while ensuring auditable, robust security independent of the agent's LLM reasoning.

Key insights

External policy enforcement provides deterministic, auditable security for AI agents, independent of their internal reasoning.

Principles

• Isolate agents from the outside world with defined boundaries.
• Separate policy enforcement from agent capability development.
• Employ default-deny and "forbid wins" semantics for robust security.

Method

Define security rules in Cedar, either directly or via natural language generation. Deploy these policies to a Policy Engine, which is then associated with an AgentCore Gateway to intercept and evaluate all agent-to-tool requests at runtime.

In practice

• Use Cedar policies for fine-grained, identity-aware access control.
• Implement "forbid" rules to hard-stop dangerous input patterns.
• Test policies in LOG_ONLY mode before enforcing in production.

Topics

• AI Agents
• Amazon Bedrock AgentCore
• Policy Enforcement
• Cedar Language
• LLM Security

Code references

• awslabs/amazon-bedrock-agentcore-samples
• awslabs/amazon-bedrock-agentcore-samples

Best for: AI Engineer, MLOps Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.