Skip to main content
aaas.diy
Article
LLMGenAIComplianceSecurityPolicy-as-CodeOPA

Externalize LLM Compliance with a Policy Engine

Standalone policy engines are replacing ad-hoc prompt guards for enterprise LLM compliance. They decouple rules from code, are reusable across models, and are easier to test and audit, giving you a centralized control plane for AI risk.

intermediateLess than 4 hours6 steps
The play
  1. Audit Your Current LLM Guardrails
    Review your LLM applications. Identify any compliance logic (like PII filtering or topic restrictions) implemented directly in your application code as Python functions or as complex, non-deterministic system prompts. Note their brittleness and lack of reusability.
  2. Select a Policy-as-Code Language
    Choose a declarative policy language to define your rules. Open Policy Agent (OPA) with its language Rego is a mature, cloud-native standard. AWS's Cedar is another emerging option. For this pack, we'll use Rego to externalize our compliance logic.
  3. Draft a Declarative PII Redaction Policy
    In a new `.rego` file, create a simple policy that denies any LLM output containing a social security number pattern. This rule is explicit, version-controllable, and completely separate from your application logic.
  4. Integrate the Policy Engine at the Inference Gateway
    Modify your application's LLM call sequence. Before returning the LLM's response to the user, make a synchronous API call to your policy engine (e.g., an OPA sidecar). Pass the LLM input and output as a JSON object. Enforce the `allow`/`deny` decision returned by the engine.
  5. Unit Test and Deploy Your Policy
    Write unit tests for your Rego policy to verify its behavior against known good and bad outputs. This provides auditable proof of control effectiveness, which is impossible with prompt-based guards. Once tests pass, deploy your policy independently of your application code.
  6. Build a Production-Grade Compliance Layer
    Now that you've implemented the basic pattern, use the DIY package to build a more robust, production-ready compliance service with structured logging, performance metrics, and advanced policy examples. This will serve as your centralized control plane for all GenAI applications.
Starter code
Stop writing brittle, custom Python scripts or complex system prompts to filter LLM I/O; externalize compliance to a dedicated, testable service that simplifies your application code and reduces maintenance.
Externalize LLM Compliance with a Policy Engine — Action Pack