What makes an AI gateway "secure" rather than just a proxy?

A proxy forwards requests and may add caching, retries, and token accounting. A secure AI gateway makes an authorization decision on each request and produces evidence of that decision. The distinguishing properties are identity binding (the request carries a verified subject and role), per-request policy evaluation, fail-closed behavior when policy cannot be evaluated, classification of the data inside the prompt, coverage of every model endpoint the enterprise uses, an audit record written outside the calling application's custody, and a latency budget small enough that the control stays enabled in production. A product missing any of these has a documented bypass.

Is a secure AI gateway the same as an AI firewall?

They answer different questions. An AI firewall makes a content judgment: does this prompt or response match a pattern I am configured to block, such as a prompt-injection signature or a jailbreak string. A gateway makes an authorization judgment: is this identity permitted to make this call, under this policy, with this data. Content filtering can be one input into the gateway's decision. The gateway additionally owns routing, which is where data-residency policy lives, and the per-decision audit record. I draw the full distinction in AI firewall vs AI gateway vs AI proxy.

Should an AI gateway fail open or fail closed?

Fail closed, for any deployment where the AI traffic touches regulated or sensitive data. A gateway that forwards requests when its policy engine is unreachable turns every policy-service outage into an authorization bypass, and an attacker who can degrade the policy service can therefore bypass the control. The cost of fail-closed is availability, and the correct engineering response is to make the policy path reliable rather than to soften the posture. Cache compiled policy locally, replicate the decision point, and keep the evaluation path free of network dependencies.

Does a secure AI gateway replace DLP?

No. It covers a channel that DLP is structurally unable to inspect. Network DLP evaluates traffic below the TLS layer and classifies documents at rest or in motion; an LLM prompt is an assembled context window inside an encrypted HTTPS POST to a provider API. The gateway terminates that request, parses the payload, and classifies the prompt content, which gives the enterprise a classification decision that DLP never had visibility to make. The two controls sit at different layers and cover different data paths.

How much latency does an AI gateway add?

The number to ask for is p95 with classification enabled, not median with it off. In internal DeepInspect testing, enforcement overhead measures under 50 ms. That sits against LLM inference of 500 ms to 5 seconds, so the enforcement decision is a small fraction of the response the user is already waiting for. The practical consequence matters more than the number: a control with a small, predictable overhead survives capacity reviews, and a control with an unpredictable one gets disabled the first time a latency incident is attributed to it.

Secure AI Gateway: The Seven Properties That Decide Whether It Is a Control

An AI gateway that routes traffic to model endpoints, caches responses, and counts tokens is a proxy with a billing feature. Nothing about that architecture makes it a security control. The word "secure" earns its place only when the gateway can make an enforceable decision about a request and produce evidence that survives the failure of everything around it.

I want to lay out the seven properties that separate the two, because the buying question is not which gateway has more features. It is which gateway can still tell you the truth after something goes wrong.

Identity binding

The gateway must know who is calling, and "who" has to be a verified identity rather than an API key shared across a team. A key is a bearer token: whoever holds it is indistinguishable from whoever was issued it. A gateway that authorizes on key possession authorizes on nothing.

Identity binding means the request arrives carrying a subject (a user, a service, an agent), the role that subject holds, and the tenant it belongs to, and that the gateway evaluates all three. NIST's AI agent identity and authorization framework calls this Pillar 1, agent identity, and the comment window on it closed April 2, 2026 (NIST NCCoE). Pillar 1 is supplied by the calling application. The gateway's job is to refuse to proceed without it.

Per-request authorization

Authentication answers who is calling. Authorization at the AI call layer answers a different question: is this specific caller permitted to send this specific prompt content to this specific model right now.

Most deployments answer the first and skip the second. That is the post-authentication gap, and it is why a fully authenticated internal user can still route regulated data into a public model endpoint. A secure gateway evaluates policy per request: per role, per route, per data classification. The decision is made before the request leaves the perimeter.

Fail-closed behavior

Ask any gateway vendor what happens when the policy engine is unreachable. There are two possible answers, and only one of them is a security control.

A gateway that forwards traffic when policy evaluation fails has converted every outage into an authorization bypass. A gateway that denies traffic when it cannot evaluate policy has an availability cost and a defensible posture. Fail-closed is the property; the engineering work is making the policy path reliable enough that fail-closed rarely fires. I set out the tradeoff in detail in fail-closed vs fail-open AI gateway.

Prompt-level data classification

Legacy DLP classifies documents. A prompt is not a document. It is an assembled context window containing fragments of retrieved records, pasted code, chat history, and user text, and it exists for the duration of one HTTP request.

Network-layer DLP runs underneath the TLS session to api.openai.com and sees encrypted bytes. It is blind to prompt content unless TLS inspection is configured for AI provider domains specifically and the payload is parsed. A secure gateway terminates that request, parses the payload, and classifies what is inside the context window before the call proceeds. Cloud Radix reports that 77% of employees using unauthorized AI admit to pasting sensitive business data into unsanctioned models, and that 86% of IT leaders are blind to those interactions (Cloud Radix).

Model-agnostic reach

A gateway that only covers one provider's endpoints protects one provider's traffic. Every enterprise I talk to runs at least three model surfaces: a commercial API, a cloud-hosted model behind Bedrock or Azure OpenAI, and something self-hosted.

Policy that applies to one and not the others produces a control with a documented bypass. Model-agnostic means the same policy, the same identity model, and the same record format apply in front of OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, and a self-hosted Llama or Mistral endpoint, in one deployment.

Write-path independence for audit records

If the application that makes the AI decision also writes the compliance log, you have a self-attestation problem. In every other regulated domain, the system under audit does not author the audit record.

Application-controlled logs fail three ways. They log successes and miss edge-case failures. They can be modified or wiped by the same system that failed. They lose the record when the process crashes after the model responds and before the log commits. A gateway that commits a signed, tamper-evident record before the response returns to the application removes all three, because the application never has custody of the write path.

A bounded latency budget

Every property above is worthless if the security team is forced to disable it under load. The latency argument is the one that decides whether a gateway stays enabled in production.

The math is favorable. LLM inference runs 500 ms to 5 seconds. Enforcement overhead in internal DeepInspect testing measures under 50 ms. A decision that costs single-digit percentage points of the model's own response time does not get switched off in a performance review. Ask for the p95, not the median, and ask what the number looks like with classification enabled.

What the seven properties add up to

Read together, they describe a policy decision point that sits at the AI request boundary, binds every call to an identity, evaluates policy per request, denies on ambiguity, understands what is inside the prompt, covers every model the enterprise uses, and writes an independent record of what it decided. That is a control. Anything short of it is a router.

DeepInspect

This is exactly what DeepInspect does. DeepInspect is a stateless proxy at the AI request boundary. Every request from an authenticated user or agent to an LLM endpoint is evaluated against identity, role, prompt data classification, and the policy attached to the route, then permitted, redacted, or denied inline. The posture is fail-closed by default.

Each decision produces a signed, per-decision audit record committed before the response returns to the calling application, so the record survives compromise or crash of that application. Because the proxy is model-agnostic and holds no long-lived provider keys, one policy set covers every model endpoint in the estate.

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