Secure AI Agents with Policy in Amazon Bedrock AgentCore

Deploying AI agents, especially in sensitive environments, comes with unique security challenges. Unlike traditional software, agents make autonomous decisions, accessing tools and data to achieve goals. This flexibility is powerful but demands robust safeguards to prevent unintended actions or data breaches. Amazon Bedrock AgentCore is stepping up to address this with its new Policy feature, offering a principled way to manage agent behavior.

This innovative solution provides developers with a powerful tool to enforce strict runtime boundaries for AI agents, ensuring they operate within predefined safety parameters. It’s particularly vital for regulated industries, where data privacy and operational integrity are paramount. To dive deeper into this game-changing feature, check out the Secure AI Agents with Policy in Amazon Bedrock AgentCore blog post.

What Policy in Amazon Bedrock AgentCore Does

At its core, the new Policy feature in Amazon Bedrock AgentCore moves security enforcement outside the agent's own code. Instead of embedding rules directly, it implements external policy enforcement through the AgentCore Gateway. This means every single agent-to-tool request is intercepted and evaluated at runtime, providing a critical layer of control.

This external approach is a significant shift. It creates a deterministic enforcement layer that operates independently of the agent's reasoning. This makes the system far more resilient to adversarial attacks like prompt injection, as the policy acts as an unyielding guardian regardless of how an agent might be manipulated. The system leverages Cedar, a specialized authorization language, to define precise, analyzable policies. With Cedar, you define who (principal) can do what (action) to which resource (where), with additional conditions in a 'when' clause.

Why External Policy Enforcement Matters

Separating policy from agent code offers several compelling advantages. Firstly, it provides auditable security boundaries. Security teams can review clear, explicit policy definitions rather than sifting through potentially complex application code to understand agent limitations. This clarity vastly improves compliance and reduces the risk of oversight.

Secondly, this separation allows developers to focus on building agent capabilities without constantly worrying about every line of tool-calling code becoming a potential security vulnerability. By offloading security enforcement to an external, deterministic layer, they can innovate faster and with greater confidence. This is crucial for environments like healthcare, where agents handle sensitive patient data and must adhere to strict access boundaries, as detailed in the Secure AI Agents with Policy in Amazon Bedrock AgentCore announcement.

How to Get Started

Getting started with Policy in Amazon Bedrock AgentCore is designed to be flexible. You can author policies directly using Cedar for fine-grained programmatic control. Alternatively, for greater accessibility, policies can be generated from plain English statements, which are then automatically formalized into syntactically correct and validated Cedar code.

Once defined, these policies are enforced by AgentCore Gateway, which intercepts and evaluates every agent-to-tool request against your defined rules. This ensures that agents only access the tools and data that their users are authorized to use, consistently and at scale. To learn more about implementation details and best practices, refer to the official AWS blog post.

Read more: Secure AI Agents with Policy in Amazon Bedrock AgentCore and enhance your AI agent's security today.