Secure AI agents with Policy and Lambda interceptors in Amazon Bedrock AgentCore gateway

2026-06-01 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Cloud Computing & IT Infrastructure · Depth: Intermediate, extended

Summary

Amazon Bedrock AgentCore gateway offers two complementary mechanisms, Policy and Lambda interceptors, to secure AI agent behavior and tool access in enterprise solutions. Policy, authored in Cedar, provides deterministic, auditable access control by evaluating requests against principals, actions, and resources, with decisions logged in CloudWatch. Lambda interceptors enable dynamic validation, payload enrichment, and response filtering through custom code executed before or after tool calls. The article demonstrates these capabilities using a lakehouse data agent, which allows insurance employees to query claims data stored in Amazon S3 Tables (Apache Iceberg) and Amazon Athena, with security enforced by AWS Lake Formation. It details how to combine interceptors for dynamic context injection (e.g., user geography from Amazon DynamoDB) with Cedar policies for declarative, geography-based access control, ensuring robust, layered security for dynamic LLM-powered workflows.

Key takeaway

For AI Architects designing secure agentic solutions on Amazon Bedrock, you should implement a layered security approach. Use AgentCore Policy for deterministic, auditable access control based on identity claims and resource ARNs, especially for critical "kill switch" scenarios. Complement this with Lambda interceptors to handle dynamic requirements like token exchange, external data lookups (e.g., user geography from DynamoDB), and payload transformations. This combination ensures robust governance and compliance for your LLM-powered workflows.

Key insights

Secure AI agents by combining deterministic Policy with dynamic Lambda interceptors in Amazon Bedrock AgentCore gateway.

Principles

• Policy provides deterministic, auditable access control.
• Interceptors enable dynamic validation and payload transformation.
• Layered security combines both for robust agent governance.

Method

Implement a REQUEST interceptor to enrich context (e.g., geography, tenant credentials) before Cedar Policy evaluates the enriched request, then use a RESPONSE interceptor for filtering.

In practice

• Use Cedar for role-to-tool access restrictions.
• Implement JWT-to-IAM token exchange via interceptors.
• Filter tool lists dynamically with RESPONSE interceptors.

Topics

• Amazon Bedrock AgentCore
• AI Agents
• Access Control
• Cedar Policy Language
• Lambda Interceptors
• AWS Lake Formation

Code references

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

Best for: AI Engineer, AI Architect, AI Security Engineer

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