Keep AI within policy — and prove it

Gatelyr enforces what's allowed and what isn't on every AI request — which models can be used, what data can be shared, who can access which parts of the platform, and when a human needs to approve something — and keeps an audit-ready record of every enforcement decision and policy change, so a rule is applied automatically to each call and provable after the fact, not a paragraph in a document nobody checks.

The problem

Most companies have AI policies on paper — which models are approved, what data can't be shared, when someone has to sign off — but nothing actually enforcing them on a live request, and no record to hand an auditor showing what was allowed, blocked, or approved, and why.

How it helps

What this means day to day

Prove compliance with an audit-ready record

Show a regulator or auditor exactly what was allowed, blocked, or approved — and why — from a complete history of enforcement decisions and policy changes.

Control which models see which data

Keep regulated or sensitive workloads on approved models, and off the ones you haven't cleared.

Give people exactly the access they need

Users, groups, and role-based permissions across every part of the platform, so people can see and change only what their role allows.

Set policy per organization or business unit

Configure detection and enforcement independently for each org or unit — a regulated division and an internal lab don't have to share one policy.

Require a human sign-off where it matters

Flag certain requests or actions for approval before they proceed.

Get told when something important happens

Automatic alerts on the events that matter — a security detection, a budget threshold crossed — routed to the people who need to know.

Know who changed what, and when

Keep a clear, reviewable history of every policy change over time.

Turn a policy into something actually enforced

Move rules out of a document nobody re-reads and into something applied automatically to every request.

A policy console showing enforced controls — regulated data restricted to approved models, PII redacted, over-budget spend blocked, and changes requiring approval — each applied automatically, with roles and an audit-ready change history — Policies enforced automatically on every call — with roles and an audit-ready change history.