Is an AI gateway the same as an AI firewall?

No. An AI gateway is primarily a routing and observability layer over multiple LLM providers. An AI firewall is primarily a content inspection layer over prompts and responses. Many products bundle both functions, which is why the categories blur in RFPs. The distinction matters when the buying team scores against specific controls: the firewall answers "did this payload violate policy," the gateway answers "which model handled this and how much did it cost."

Can I use my WAF as an AI firewall?

A WAF provides partial coverage. It inspects HTTP payloads at layer 7 and can catch some structural prompt-injection patterns. Its payload checks miss semantic PII classification, verified binding to the natural person behind the calling application, and model-specific policy. The WAF sits on the perimeter of the enterprise, not on the perimeter of the AI request path.

Where does DLP fit in this picture?

Traditional DLP inspects data movement across enterprise boundaries: email, cloud storage, endpoints. Network-layer DLP is blind to the semantic content of prompts that flow through TLS-encrypted API calls to OpenAI or Anthropic. AI-specific DLP integrated into a firewall or proxy at the AI request boundary is the control point that inspects the actual prompt and response payloads. The LLM DLP pillar covers the differences.

Does the proxy have to sit inline?

Inline placement is required for enforcement. An offline proxy that receives copies of requests for analysis after the fact can produce detection, not prevention. Inline proxying with fail-closed defaults is the pattern that stops a violating request before it reaches the model. The inline enforcement architecture piece covers the latency and reliability trade-offs.

What is the latency budget for an inline AI proxy?

Sub-50ms at the p95 is the operational target for enterprise deployments. The proxy adds one network hop and one policy evaluation to a call whose end-to-end latency (LLM inference plus network) already runs into the hundreds of milliseconds. The gateway latency benchmark covers the measurement methodology.

Separate products are unnecessary. A single identity-aware AI proxy with content inspection and multi-provider routing collapses the firewall, gateway, and proxy roles into one control point. The reason to keep them separate is when an existing gateway (from a vendor like Kong or LiteLLM) is already in production and the security team is layering identity-aware policy enforcement in front of it.

AI Firewall vs AI Gateway vs AI Proxy: The Category Distinctions Buying Teams Keep Blending Together

An AI firewall checks prompt and response content. The AI gateway routes and governs calls across model providers. The proxy carries the HTTP request to the model and back. These categories show up on the same RFP shortlist because vendors use overlapping labels, yet they answer different control questions. I want to walk through where each one sits in the request path, what it enforces, and when identity-aware policy evidence changes the buying decision.

The categories overlap because vendors label their products with whichever term the buyer is searching for. That does not change the architecture underneath.

TL;DR

An AI firewall examines prompt and response content. The AI gateway centralizes provider routing and operational controls. The proxy carries the HTTP exchange, and an identity-aware proxy can attach a verified caller and policy decision to each request. Select the control point that supplies the evidence and enforcement the deployment requires.

AI firewall

An AI firewall inspects prompts and responses for policy violations before or after the model call. The unit of enforcement is the payload: prompt text, generated output, structured tool arguments, retrieved context. AI firewalls score payloads against classifiers for prompt injection, PII leakage, jailbreak patterns, and unsafe content categories. The output is a pass, block, or transform decision.

The category came out of two adjacent lineages. The web application firewall (WAF) heritage produced signature-based prompt-injection detection. The DLP heritage produced content classifiers for regulated data types. Modern AI firewalls fuse both with LLM-based judges for higher-context detection.

Where an AI firewall sits in the request path determines whether it can enforce anything. When deployed as a middleware library inside the application, the firewall enforces only what the application decides to route through it. When deployed as an inline control at the HTTP boundary, the firewall enforces every request the calling identity issues, regardless of application discretion.

AI gateway

An AI gateway is an aggregation layer over multiple LLM providers. It exposes a single API endpoint to internal applications, routes calls to the appropriate provider (OpenAI, Anthropic, Bedrock, Vertex), and centralizes concerns that would otherwise scatter across applications: rate limiting, cost attribution, retry logic, caching, model fallback, per-team quotas.

The AI gateway pillar covers the architectural patterns. The distinguishing property is provider abstraction. An enterprise running workloads across three model providers should route through the gateway so the vendor mix can shift without application code changes.

Gateways do not necessarily inspect content. A minimal AI gateway is a routing and observability layer. Content inspection, policy enforcement, and audit logging are extensions the gateway can host or delegate to a firewall running alongside. The June 2026 CVE wave in LiteLLM (five new vulnerabilities disclosed June 22, led by CVE-2026-12773, a CVSS 7.3 authentication bypass, on top of the CVE-2026-42271 RCE CISA added to its KEV catalog June 8) showed that a gateway's authentication layer has its own attack surface. A gateway that stores long-lived provider keys becomes a compromise multiplier the moment its auth bypasses.

AI proxy

An AI proxy is the HTTP transport component that terminates the client TLS session, forwards the request to the upstream LLM API, and hands the response back. In one sense every AI gateway is an AI proxy: it sits inline on the HTTP request path. In another sense the term "AI proxy" narrowly describes the transport plumbing without the surrounding policy or routing logic.

Two proxy design choices decide what policy the surrounding system can enforce. First, stateful versus stateless. A stateful proxy holds session context (conversation history, cached embeddings, provider credentials) inside the proxy process. A stateless proxy holds none of that; each request stands alone with the identity claim it carries.

Identity binding is the second decision. A proxy that terminates TLS but forwards the request with a shared service account (the classic API-gateway pattern) cannot bind the request to the natural person or agent behind it. A proxy that requires a verified identity claim on every request, and forwards that identity to the audit log, produces per-decision evidence a regulator can inspect.

Feature comparison

The categories overlap enough that the operational differences need a direct list.

Prompt and response inspection: An AI firewall makes payload inspection its main job. A gateway can delegate it. An identity-aware proxy can inspect at the HTTP boundary.
Provider routing: An AI gateway abstracts and routes across providers. A proxy can support routing as a secondary capability. A firewall focuses on the payload.
Rate limits and quotas: Gateways and proxies commonly enforce them. A firewall may leave them to another layer.
Request identity: A proxy can require a verified identity claim for each request. Firewalls commonly rely on application context, while gateway support varies by product.
Audit evidence: A proxy can emit a tamper-evident decision record. Gateways often make logging optional; a firewall's record can remain application-controlled.
Failure behavior: Firewall behavior is configurable. Gateways often favor availability. A policy proxy can fail closed when a required check cannot run.
Latency planning: Firewall overhead commonly falls in the 20-200 ms range. Gateway overhead can be 5-50 ms. A proxy needs a documented budget for the added policy check.
Detection surface: Firewalls classify payloads. Gateways aggregate traffic. Proxies inspect the HTTP session with the caller's identity context.
Regulatory evidence: Per-decision records with verified request identity give an auditor more usable evidence than an application-level activity log.

The three categories converge when the deployment requires identity-aware, model-agnostic, inline enforcement with per-decision audit evidence. That deployment pattern is what the EU AI Act, HIPAA, and NIST AI RMF each land on for different reasons.

Regulatory framing

EU AI Act Article 12 requires automatic, lifetime logging of high-risk AI system events. Article 19 requires those logs to include the identity of natural persons involved. Application-controlled logs fail this requirement when the calling identity is a shared service account. The EU AI Act Article 12 logging pillar walks through the mechanism.

Even after the Council's June 29, 2026 approval of the Digital Omnibus on AI (which deferred standalone Annex III high-risk obligations to December 2, 2027), Article 50 transparency obligations still take effect August 2, 2026, and the AI-content labeling grace period shortened from six months to three (now December 2, 2026). The logging and identity-binding controls a proper proxy layer enforces are due in 2026 regardless of the high-risk deferral.

HIPAA covered entities running LLM inference on protected health information need audit trails at the request level, not just the application level. The accounting-of-disclosures requirement maps to per-decision evidence.

The NIST AI RMF frames measurement and management as ongoing governance functions. Per-decision evidence gives incident review a record of the request, policy, and outcome. A firewall that inspects payload without binding identity leaves that record incomplete.

DeepInspect

This is the gap DeepInspect closes. DeepInspect sits at the AI request boundary as an external enforcement layer: identity-aware, deterministic, and independent of model behavior. Every request is bound to a verified identity claim before it reaches the model. Every response passes back through the same layer. The record of who asked, which model answered, which policy applied, and which data classification the request touched lands in a tamper-evident audit log.

The proxy architecture is stateless. There are no long-lived provider credentials to steal, no session storage to compromise, no shared cache to poison across tenants. Failure defaults are fail-closed. Latency targets are sub-50ms at the p95.

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