What is the difference between an AI governance platform and an LLM gateway?

AI governance platforms (GRC tools) manage inventories, risk assessments, and compliance workflows around AI systems. An LLM gateway sits in the request path and enforces policy on each call in real time — allow, redact, block, or route to a human — before the request reaches a model provider. They answer different questions and are often used together.

Do I need runtime enforcement if I already have an AI governance framework?

A framework defines what should happen; runtime enforcement is what makes it happen on every call. Without an enforcement point in the request path, policy adherence depends on each application team implementing rules correctly in their own code, which drifts over time.

What should an AI governance tool log for audits?

Every request outcome — including blocks and pending approvals, not just successes — with correlation IDs, timestamps, the matched policy and its version, detector findings, the decision, and the reasoning behind it, stored append-only so a decision can be reconstructed months later.

Buyer's guide

AI governance tools compared: GRC platforms vs runtime gateways

“AI governance tool” covers three very different product categories. They are complementary, not interchangeable — and choosing the wrong one leaves a real gap. This guide explains what each category actually does, where it stops, and how to decide what you need first.

The short answer

GRC platforms document your AI risk; data governance suites find your sensitive data; runtime gateways enforce policy on each live model call. If your immediate risk is applications sending ungoverned prompts to a model provider today, start at the runtime layer — it is the only category that can stop a call before it happens.

AI GRC & compliance platforms

“Are our AI systems inventoried, risk-assessed, and documented for regulators?”

Tools in this category — such as Credo AI, OneTrust, and Holistic AI — focus on governance workflows: system inventories, risk classifications, control mapping to frameworks like the EU AI Act and NIST AI RMF, and audit reporting.

What it's good at

System inventory and lifecycle tracking across the organization
Control mapping to regulatory frameworks and standards
Executive and auditor-facing reporting

Where it stops

–They document policy rather than enforce it on live traffic
–No position in the request path — an app can call a model without the platform ever seeing the call
–Sensitive-data handling depends on each application implementing it correctly

Data governance & discovery suites

“Where is our sensitive data, and who is using it?”

Platforms such as Collibra and BigID extend data catalogs and data-loss-prevention programs into AI use cases, discovering sensitive data and tracking how it flows into models and pipelines.

What it's good at

Sensitive-data discovery and classification at rest
Lineage from data sources into AI pipelines
Mature enterprise data-governance workflows

Where it stops

–Visibility is primarily at rest; a prompt sent from a live application is outside their boundary
–No per-request decision (allow / redact / block / approve) at call time
–Not a drop-in for application teams shipping LLM features

Runtime LLM gateways with governance

“Is this specific call allowed, under which policy, and what did it send?”

This is the category Airlock belongs to. The gateway sits between your applications and the model provider, so every request is matched to a policy, inspected for sensitive data, optionally held for human approval, and written to an immutable audit log — before it reaches the provider.

What it's good at

Policy enforced on every call, not per team or per repo
Sensitive-data detection with redact / warn / block / approve actions
Human-in-the-loop approval for high-risk requests
Append-only audit events with policy version, findings, and reasoning

Where it stops

–Does not replace GRC workflows like risk assessments and control mapping
–Covers traffic routed through the gateway — shadow usage outside it still needs discovery

Five questions to ask any AI governance tool

Can it act on a live request, or only report after the fact?

If a tool cannot allow, redact, block, or hold a call in flight, enforcement lives in application code — where it drifts.

Is the default fail-closed?

Requests that match no policy should not silently reach a provider. Ungoverned traffic should be impossible, not merely logged.

Does the audit record explain decisions?

You need the matched policy, its version, detector findings, and reasoning — not just a success/failure row.

Can a human review edge cases without a second pipeline?

Approval should pause the same request and resume it after review, so the reviewed call and the delivered call are identical.

Does adoption require rewriting applications?

An OpenAI-compatible gateway like Airlock lets existing clients switch by changing the base URL and API key.

Where Airlock fits

Airlock is a runtime governance gateway for OpenAI-compatible applications. Point your existing client at Airlock, connect your own provider key, and every call gets a policy match, sensitive-data inspection, optional human approval, and an immutable audit event — with a fail-closed default for anything no policy covers. It pairs with, rather than replaces, GRC tooling when you need inventories and regulatory reporting.