Why does the gateway need to be stateless?

Statelessness simplifies horizontal scaling, fault tolerance, and deployment in regulated environments. A stateless gateway can be replicated behind a load balancer without coordination state. It can be restarted, replaced, or migrated without data loss. Statelessness also reduces the attack surface because the gateway does not retain prompt content beyond the call. Audit records persist in durable storage outside the gateway; the gateway writes and forgets. The audit storage is a separate concern with its own integrity, retention, and access controls.

What identity assurance level does the gateway require?

The gateway consumes the identity assurance level the upstream IAM provides. For high-risk AI use cases under the EU AI Act, the assurance level should be at least the level required for the underlying business decision, which is typically multi-factor authentication for human users and short-lived credentials for agents. The gateway does not authenticate the user itself but can reject requests where the assurance level is below a configured threshold for a given policy.

Can the gateway enforce policy on responses from the model, not just on prompts?

Yes. The gateway inspects the response under a response-side policy before returning the response to the calling application. The response-side policy typically catches PII or regulated content that the model produced from its training data, content that violates the deployment's output rules, or prompt-injection consequences that surface in the model's output. The response-side enforcement closes a category of risk that prompt-only enforcement misses.

How does the gateway handle calls from autonomous agents?

Calls from autonomous agents carry agent identity in the same structured metadata field that human user identity uses. The policy decision point evaluates the per-agent role, the delegation chain (which human or system delegated to this agent), and the per-policy authorization for the agent class. The audit record captures the agent identity and the delegation chain, which produces action lineage under NIST Pillar 3.

What happens when the gateway is unavailable?

The architecture defaults to fail closed. When the gateway is unavailable, calls to the model APIs are denied at the network level. The fail-closed posture is the appropriate default for regulated environments because the cost of a denied legitimate request is bounded and the cost of an unaudited request can run into the millions of euros in EU AI Act penalties or into representation-and-warranty breaches under LL-2026-04. Operators can configure exceptions for non-regulated routes where availability

Identity-Aware AI Gateway Architecture: How Inline Enforcement Binds Decisions to Users and Agents

An identity-aware AI gateway sits inline between authenticated users or agents and the LLM APIs they call. For every request, the gateway attaches verified identity context (the natural person, the agent, the role, the tenant), evaluates per-route and per-role policies on the prompt and on the response, 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 inherit from the credential-pooling pattern used by SDKs and AI proxy frameworks. The post-authentication gap is the gap between "the user is authenticated" and "this specific request, by this user, against this data classification, is permitted." Most deployments answer the first and leave the second unanswered.

I want to walk through the building blocks of the identity-aware AI gateway architecture, where it fits relative to existing infrastructure (IAM, API gateways, DLP), the call path for a single AI request, the audit record it produces, and the operational characteristics that make it deployable in regulated environments.

What the identity-aware AI gateway is and is not

The identity-aware AI gateway is a stateless proxy. It sits at the AI request boundary, which is the network boundary between calling applications and the model API endpoints they use. The proxy operates on HTTP AI traffic. Every request is intercepted, evaluated against policy, and either permitted, redacted, or denied. Every decision is recorded with a cryptographic signature.

The gateway sits outside the model. Inference happens at the provider; generation happens at the provider. The model provider's safety layers continue to operate inside the inference process and remain a separate concern.

The gateway differs from a traditional API gateway, although it sits in the same architectural position. Traditional API gateways handle routing, rate limiting, authentication delegation, and basic transformation. An identity-aware AI gateway adds the AI-specific concerns: prompt classification, identity attribution at the API call level, per-decision audit, and policy evaluation that depends on the semantics of the prompt content.

The gateway operates at a different layer than a CASB. CASB tools see TLS-encrypted traffic from the outside at the network or session level. The gateway terminates TLS and inspects the prompt content, which requires sitting inline with explicit credential.

A traditional DLP and the gateway cover different ground. DLP classifies documents and structured records. The gateway classifies prompt content at the context window level, including unstructured text that DLP categories were not built for.

The architectural building blocks

The identity-aware AI gateway has six components.

Identity adapter

The identity adapter receives identity context from the calling application or from an upstream IAM system. The context includes the natural person's identifier, the agent identifier where the call is from an autonomous agent, the role and group memberships, the tenant identifier, and the authentication assurance level. The adapter binds the context to the request as structured metadata that the rest of the gateway uses.

Policy decision point

The policy decision point evaluates the request against the policy in effect. The policy expresses rules at four levels: per route (the model endpoint or capability), per role (the requester's role), per data classification (what's in the prompt), and per context (time, location, device posture). The evaluation is deterministic. The output is permit, redact, or deny.

Prompt classification engine

The classification engine inspects the prompt content for regulated data types: PII, PHI, MNPI, credentials, secrets, regulated identifiers, organization-specific patterns. The classification feeds the policy decision point and informs the redaction or block decision.

Enforcement engine

The enforcement engine applies the decision. Permit forwards the prompt unmodified. Redact replaces sensitive elements with placeholders or hashes and forwards the modified prompt. Deny stops the request and returns a structured error to the caller.

Audit record writer

The audit record writer produces a per-decision record containing the identity, the role, the policy version, the data classification, the model and version targeted, the decision outcome, the timestamp, and a cryptographic signature. The record writes before the model response returns to the calling application, which prevents the calling application from suppressing it.

Observability and telemetry

The observability layer surfaces metrics, traces, and alerts for the security operations and the engineering teams. The metrics include decision rates, policy hits, latency, and error rates by route and role.

The call path for a single AI request

The call path runs through eight steps.

The calling application receives a user action that triggers an AI call. The application assembles the prompt and the identity context.
The application sends the request to the AI gateway with identity context attached as structured metadata.
The identity adapter validates the identity context. Where the context is incomplete, the adapter rejects the call or augments the context from an IAM lookup.
The classification engine inspects the prompt and labels it with the applicable data classifications.
The policy decision point evaluates the per-route and per-role policy with the classification as an input. The decision is permit, redact, or deny.
The enforcement engine applies the decision. The audit record writer prepares the record.
The audit record is committed to durable storage before the gateway forwards the prompt to the model API.
The model API responds. The gateway optionally inspects the response under the response-side policy, applies any redaction, and returns the response to the calling application. The audit record is updated with the response outcome.

The steps from 3 through 8 typically complete in under 50 ms in production. The LLM inference time is 500 ms to 5 seconds. The gateway's overhead is invisible against the model's response time.

How the gateway fits with existing infrastructure

Most enterprises already operate IAM, an API gateway, and DLP. The identity-aware AI gateway integrates with all three.

The IAM system provides the identity context. The gateway consumes identities and groups from the IAM and trusts the assertions. The gateway does not authenticate the user; it consumes the assertion from the upstream IAM.

The traditional API gateway sits in front of internal APIs and handles routing and rate limiting for the application's external surface. The identity-aware AI gateway sits in front of the AI provider APIs (OpenAI, Anthropic, Bedrock, Vertex, Azure OpenAI, self-hosted). Both layers are useful and operate on different traffic. The AI gateway does not replace the application API gateway.

The DLP system sees document and email flows. The AI gateway sees the prompt and response flows. The two complement each other and together cover the data-loss surface for the modern enterprise.

What an identity-bound audit record looks like

The audit record produced by the gateway contains:

A verified identity for the natural person or the agent behind the request
The role and authorization context that was in effect
The tenant identifier where the deployment is multi-tenant
The data classifications that applied to the prompt
The policy version that governed the decision
The model and version targeted
The decision outcome (permit, redact, deny)
The timestamp with sufficient precision to correlate across systems
A cryptographic signature that prevents post-hoc modification

The record is independent of the application that made the request. It is committed before the model response returns. It persists regardless of the application's runtime state. The record set satisfies the per-decision evidence requirement under EU AI Act Article 12, NIST AI agent identity and authorization Pillar 3 (action lineage), Fannie Mae LL-2026-04 audit trail, HIPAA AI audit obligations, and the equivalent provisions across DORA, NIS2, ISO 42001, and the US state regimes.

DeepInspect

This is the gap DeepInspect closes for enterprises building identity-aware AI infrastructure. DeepInspect implements the identity-aware AI gateway architecture as a stateless proxy that sits inline between authenticated users or agents and any HTTP-based LLM endpoint. The same architecture covers OpenAI, Anthropic, Google Vertex, AWS Bedrock, Azure OpenAI, self-hosted Llama or Mistral, and any future endpoint exposed over HTTP.

The deployment integrates with existing IAM through standard adapters, runs alongside the existing API gateway and DLP infrastructure, and produces audit records compatible with the regulatory regimes in force from August 2026 onward. The latency overhead is under 50 ms in production tests and the architecture supports fail-closed posture by default.

If you are building an identity-aware AI control plane for a regulated environment, book a technical deep dive at deepinspect.ai.