Inline enforcement
Inline enforcement is the architectural mode where a policy decision sits inside the request path between an authenticated caller and an LLM endpoint. Every request is evaluated synchronously, and a fail-closed proxy returns either pass or block before the request reaches the model. The evaluation uses identity context, data classification, and per-route rules. Out-of-band monitoring sees the prompt only after the model has already responded, so the audit trail records what happened but the request itself already completed.
How inline enforcement works
The proxy terminates the client TLS connection, decrypts the prompt payload, runs the policy decision in the request path, and either forwards the request to the model or returns a block. The decision time stays under 50 ms in the published DeepInspect benchmark so the user-facing latency stays inside the budget that production AI applications already accept. Mandiant's M-Trends 2026 report measured a 22-second median between initial access and handoff to a secondary threat group, which is the operational reason out-of-band detection fails as a control.
Fail-closed is the architectural property that matters. When the policy decision point fails to reach a definitive decision (policy lookup error, identity claim missing, classification model timing out), the request gets blocked rather than passed through. EU AI Act Article 12 traceability obligations and NIST AI RMF map and measure functions both reference per-request enforcement evidence, and inline enforcement is what produces that evidence.
Related reading
- AI Inline Enforcement Architecture: Where the Policy Decision Sits and What It Has To Commit
AI inline enforcement runs the policy decision in the request path, before the model API call returns to the calling application. The architecture places a deterministic policy decision point between the application identity and the model endpoint and commits a per-decision audit record before the response forwards. This piece walks through the architectural components, the decision-time data shape, the failure modes the implementation has to handle, and the regulatory profile that the inline placement satisfies (EU AI Act Article 12, NIST AI agent identity and authorization Pillar 2 and Pillar 3, Fannie Mae LL-2026-04, DORA Article 6).
- Identity-Aware AI Gateway Architecture: How Inline Enforcement Binds Decisions to Users and Agents
An identity-aware AI gateway sits at the AI request boundary, attaches verified identity context to every model API call, evaluates per-route and per-role policies, and commits a per-decision audit record before the model response returns to the calling application. The architecture closes the post-authentication gap that most enterprise AI deployments have inherited from the credential-pooling pattern used by SDKs and proxy frameworks. This piece walks through the architectural building blocks, the call path, the audit primitives, and where the identity-aware gateway sits relative to existing IAM, API gateway, and DLP infrastructure.