Enforcing one AI policy across LiteLLM, Kong, and Azure API Management at once

Where native policy starts and stops

LiteLLM, Kong, and Azure API Management each ship their own way of expressing policy: LiteLLM proxy settings, Kong plugins, and Azure API Management policy XML. Each is a reasonable tool for its own layer. The trouble starts when one organizational policy has to live in all three. A rule such as "this model family is allowed for this team, personal data is redacted before it leaves, and this tool call needs human approval" gets re-expressed three times, in three vocabularies, usually by different people on different schedules.

That duplication has predictable effects. Rules diverge as one layer is updated and another is not. A control that one layer can express natively may have no direct equivalent in another, so the weaker layer quietly becomes the effective policy for the traffic that uses it. Any path that avoids all three gateways is not covered by any of them. And each layer records decisions in its own format, so no single record shows what was allowed and what happened.

The practical conclusion is that native configuration is best treated as an enforcement mechanism, not as the place where policy is defined. NIST's API protection guidance makes a similar point about enforcement that is split across many points: it becomes harder to ensure policy is applied consistently and correctly.

Separate policy definition from enforcement

NIST's reference models for microservice access control separate the function that decides from the function that enforces. A policy decision point evaluates the rules. A policy enforcement point sits in the request path, applies the decision, and receives policy updates. The enforcement point should be separately deployable and controllable from the application, so a policy change does not require an application change.

Express the policy in attributes rather than in lists tied to specific users or endpoints. Attribute-based policy is written without prior knowledge of the users and resources it governs, and users and resources are assigned attribute values independently. For AI traffic, that means rules like "requests from workloads labeled X may use models labeled Y" instead of per-gateway route and key mappings.

Ownership belongs in governance. The NIST AI RMF GOVERN function applies across all stages of AI risk management, so who writes the policy, who approves changes, and how versions are retired should be settled once, outside the gateway configurations.

Three ways to apply one policy to three gateways

NIST describes two enforcement styles: native local configuration, and proxy extensions that call an external authorization server. Combined with a shared enforcement layer, that gives three patterns. Choose based on what each gateway can actually support in your deployment, and on how much consistency you need to demonstrate.

• Per-gateway translation: Compile the central policy into native settings for each gateway. No new runtime component, but three enforcement implementations to keep aligned, and any rule a layer cannot express is lost in translation.
• Callout to an external decision service: Each gateway asks one service for a per-request decision and enforces the answer locally. Decision logic is shared, while enforcement behavior, such as how redaction is performed, still varies by gateway.
• Shared enforcement layer in the execution path: One enforcement point sits where requests to models and tools pass, regardless of which gateway they came through. This is the easiest to monitor and audit, but only for traffic that traverses it.

Verify consistent enforcement

Treat consistency as something you test, not assume. Build a small suite of requests that exercise each decision type: a permitted model call, a call to a model that is not allowed, a prompt containing data that must be redacted, and a tool call that requires approval. Send each through every path and compare three things: the decision, the transformed payload after redaction, and the evidence record. Differences in any of them point to drift or to a path that is not governed.

The common failure modes are specific. Bypass paths, such as a direct provider key in an application or a route that skips the gateway, are not covered by whatever sits on the other paths. Redaction can differ if each layer implements it separately, which is an argument for performing it in one place. Identity context can differ if one gateway passes a team attribute and another passes only an API key. Policy versions can differ if updates are applied to some layers first.

Re-run the suite after every policy change and every gateway upgrade. A single enforcement point makes this tractable because verification covers all traffic traversing it. The corollary is that you must keep confirming nothing routes around it.