Ensuring Safe AI Agent Deployment in Regulated Industries
Understanding the Complexities of AI Agent Safety
The Missing Layer: Why Agents Require External Policy Enforcement
Cedar: A Language for Deterministic Policy Enforcement
Implementing Policy in Amazon Bedrock AgentCore
Applying Policy to a Healthcare Appointment Scheduling Agent
Getting Started with Amazon Bedrock AgentCore
Prerequisites for Policy Implementation
Steps for Effective Policy Implementation
Identity-Based Policies: Ensuring User-Specific Access
Read vs. Write Separation: Balancing Access Control
Risk Controls on Scheduling: Preventing Abuse
Testing Policy Enforcement: Verifying Compliance
Clean Up Resources: Managing Costs and Maintaining Organization
Conclusion: The Importance of Deterministic Policy Enforcement
Next Steps: Resources for Further Learning
Acknowledgments: Recognizing Contributions
About the Authors: Meet the Experts
Deploying AI Agents Safely in Regulated Industries
In today’s digital landscape, deploying AI agents in regulated industries poses unique challenges. As these agents gain autonomy, they can access sensitive data or perform critical transactions, introducing significant security risks. Unlike traditional software, AI agents make decisions independently, invoking tools and adapting their reasoning based on environmental data and user interactions. This autonomy is what makes agents both powerful and potentially hazardous, highlighting the need for robust security measures.
The Importance of Defining Boundaries
A useful way to conceptualize AI safety is by envisioning barriers around an agent. These walls dictate what the agent can access, interact with, and influence within its environment. Without established boundaries, AI agents with capabilities such as sending emails, querying databases, executing code, or initiating financial transactions can create vulnerabilities. Issues may arise, including data exfiltration, unauthorized access, or even prompt injection attacks.
To tackle these challenges effectively, implementing a reliable policy framework is essential. This is where Amazon Bedrock’s Policy in AgentCore comes into play, providing a principled method to enforce boundaries at runtime and scale.
A Practical Application: Healthcare Appointment Scheduling
In this post, we illustrate how Policy in Amazon Bedrock AgentCore can be applied using a healthcare appointment scheduling agent. This domain is particularly sensitive, requiring agents to handle protected health information (PHI) while adhering to strict access regulations and business rules.
Our goal is to demonstrate how to transform natural language descriptions of business rules into Cedar policies. These policies enforce fine-grained, identity-aware controls that grant agents access only to authorized tools and data. Utilizing the Policy features through AgentCore Gateway ensures every agent-to-tool request is intercepted and evaluated at runtime, adding a crucial layer of security.
The Need for External Policy Enforcement
Securing AI agents is inherently more complex than securing traditional software applications. The aspects that empower agents—such as their open-ended reasoning and adaptability—can also lead to unpredictable behaviors. Agents rely on large language models (LLMs), which are prone to issues like hallucination and lack a clear separation between trusted instructions and incidental text. This makes them vulnerable to adversarial attacks that can manipulate their behavior.
Traditional approaches typically embed security policies within the agent’s code, but this introduces a significant risk. The agent’s behavior is only as safe as the security measures coded into it, necessitating careful code reviews across potentially vast codebases. In contrast, Policy in Amazon Bedrock AgentCore offers a solution by externalizing policy enforcement. Policies are evaluated before any tool invocation, providing a safeguard against unforeseen vulnerabilities or programming errors.
Introducing Cedar: The Language for Deterministic Policy Enforcement
To support external policy enforcement effectively, a robust policy language is essential. Cedar, the authorization language used by Policy in Amazon Bedrock AgentCore, offers machine efficiency and human readability. Each policy specifies a principal (the user), action (what they can do), and resource (what they can access), along with specific conditions under which these actions are permitted.
For example, a policy to allow only a user named Alice to view a specific photo can be written as:
permit( principal == User::"alice", action == Action::"view", resource == Photo::"VacationPhoto94.jpg");Cedar’s semantics provide a straightforward way to formulate policies while maintaining rapid evaluation speeds. This ensures that policies can be assessed without side effects, which allows for safe evaluations even when policies are authored by untrusted sources.
Practical Implementation of Policy in Amazon Bedrock AgentCore
To explain how this works in practice, we’ll look specifically at the healthcare appointment scheduling agent. This AI system is responsible for tasks like checking immunization schedules and booking appointments, meaning it must protect sensitive patient data while maintaining operational integrity.
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Setting Up Policy Engines: First, create a policy engine to host the necessary policies. Policies can either be authored directly in Cedar or generated from natural language statements, making policy creation accessible and straightforward.
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Creating Policies: Utilize the Cedar policy language to define rules that allow or forbid actions based on user identity, operational context, or time constraints. For instance, policies can ensure that patients can only access their own medical records and prevent appointment scheduling outside of designated hours.
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Testing Policies: Once the policies are created and associated with the agent, conduct testing to verify that the policy enforcement behaves as expected. For example, if a patient tries to access their own record, the policy should permit access; however, an attempt to access another patient’s record should be denied.
Conclusion
AI agents are only as trustworthy as the boundaries in which they operate. By utilizing Policy in Amazon Bedrock AgentCore, industries can enforce these boundaries deterministically, ensuring that security measures are not solely reliant on the agent’s reasoning. This separation of operational capability and security enforcement forms a robust foundation for deploying AI agents securely in regulated sectors.
Next Steps: Interested in adding deterministic policy enforcement to your applications? Check out the Policy Developer Guide for comprehensive instructions and best practices for integrating Cedar policies into your agentic systems. Have questions or want to share your experiences? Engage with us in the comments or connect through community forums!