AI Governance

What is an AI gateway?

How an AI gateway controls enterprise AI usage before a request reaches a model.

Short answer

An AI gateway is a control point that sits between users or applications and generative AI models. It inspects each request, classifies sensitive data, and applies policy — allowing, redacting, routing, blocking or requiring human approval — before anything reaches the model, then records the decision. It gives enterprises runtime control and an audit trail over AI usage.

By Manoharan Mudaliar

Founder & CEO, ThreatLens

Enterprise Cybersecurity Leader | AI Governance

Last reviewed: 12 July 2026

As employees, applications, Microsoft Copilot, AI assistants and autonomous agents increasingly send prompts, files and business context to AI models, organisations have lost a clear boundary between sensitive enterprise data and the systems processing it. Traditional network and web controls do not understand an AI request — they see a connection to a domain, not the confidential contract, source code or customer record inside the prompt. An AI gateway restores that boundary. Think of it as a control plane for AI requests — every interaction is evaluated before it reaches an AI model.

The gateway is a runtime control point placed in the path of AI traffic. Unlike a proxy that only allows or blocks a destination, it interprets the content and intent of each request: what data it contains, who is sending it, which model it is bound for, and whether organisational policy permits it. That lets an organisation adopt AI broadly while keeping specific, provable control over how sensitive information is used.

Because every decision is evaluated against policy and written to a record, an AI gateway also produces the evidence that security, risk and compliance teams need — a per-request account of what was allowed, redacted, routed, blocked or approved, and why.

How a request flows through the gateway

Employee, app or agent
ThreatLens AI Gateway
Classify data
Evaluate policy
Decision: allow · redact · route · block · approve
Approved AI model
Immutable governance log

Every AI request is evaluated before it reaches a model, and the decision is recorded.

How an AI gateway works

1
A request is made — A user, application, copilot or agent submits a prompt, file or context bound for an AI model.
2
The gateway intercepts it — The request is evaluated at runtime before it reaches the model provider.
3
Content is classified — Sensitive data, secrets, source code and regulated information are identified in the prompt, files and context.
4
Policy is evaluated — Rules consider the user, the data classification, the destination model and the context of the request.
5
A decision is enforced — The request is allowed, redacted, routed to an approved model, blocked, or held for human approval.
6
The decision is recorded — The outcome and its evidence are written to an immutable log for audit and investigation.

Key controls

Data classification: Identify sensitive information — PII, secrets, source code, regulated data — inside prompts, files and context.
Redaction: Remove or mask sensitive values so a request can proceed safely instead of being blocked outright.
Model routing: Send requests to approved, private or region-appropriate models based on policy.
Blocking & approval: Stop high-risk requests, or hold them for a human to approve, before they reach a model.
Output controls: Inspect model responses — not just inputs — for sensitive or non-compliant content.
Identity-aware policy: Apply different rules by user, group, role or application.
Prompt-injection defence: Guard against attempts to manipulate the request or the model’s response.
Immutable evidence: Record every decision and its rationale for governance, audit and investigation.

AI gateway vs. secure web gateway

	AI gateway	Secure web gateway
Governs	ChatGPT, Claude, Gemini, Copilot, Bedrock, Azure OpenAI and other models	Websites and web categories
Understands	AI prompts, files, data classes and model destinations	URLs, domains and network traffic
Operates on	The content and intent of an AI request	Web and network connections
Controls	Allow, redact, route, block or approve — per request	Allow or block a site or category
Evidence	Per-request AI decision with data classification	Web access logs
Primary risk addressed	Sensitive data reaching AI models; ungoverned AI use	Malware, phishing, unsanctioned websites

A real scenario

An employee pastes a customer contract containing personal information and commercial pricing into a public chatbot to get a quick summary. Without a gateway, the confidential document leaves the enterprise boundary. With one, the request is classified as confidential, the customer name and contract value are redacted, the redacted prompt is routed to an approved model, and the decision is recorded. The employee still gets their summary — the sensitive data never leaves.

How ThreatLens implements the AI gateway

ThreatLens AI Gateway is a policy enforcement layer that evaluates AI requests before they reach a model. It can allow, redact, route, block or require approval based on the user, data classification, destination model and organisational policy — and it writes immutable evidence for every decision, across employees, applications, copilots and agents.

See the AI Gateway in action

Frequently asked questions

What is the difference between an AI gateway and an API gateway?

An API gateway manages and secures API traffic between services. An AI gateway governs the content and intent of AI requests — classifying data and applying policy before a prompt reaches a model. They solve different problems and can coexist.

Can an AI gateway govern ChatGPT, Claude, Gemini and Copilot?

Yes. An AI gateway applies policy to a request regardless of the destination model, so the same controls can span multiple providers and copilots.

Is an AI gateway the same as AI DLP?

No. The AI gateway is the enforcement layer; AI DLP is the sensitive-data protection capability that operates through it.

Does an AI gateway slow down AI usage?

A well-designed gateway adds a policy-evaluation step, not a bottleneck. Low-risk requests pass through, while only sensitive ones are redacted, routed or held for approval.

Where does an AI gateway sit?

Between users or applications and the AI models, as a runtime control point — so it can act before data reaches a provider and record the decision.

Does an AI gateway replace Microsoft Purview?

No. Microsoft Purview classifies and governs enterprise data; an AI gateway enforces runtime policy when that data is used with AI. They are complementary — Purview labels the data, and the gateway acts on it at the moment of an AI request.

Sources

About the author

Manoharan Mudaliar