AI controls that enforce before they report

AI controls are the measures that bound what an AI system is allowed to do — and that prove what it did. The ones that matter are enforced before execution. A control that only reports after the fact isn't a control; it's a record of something you couldn't stop.

As organizations move AI from pilots to production, "what controls do you have on your AI?" becomes a real question from security, finance, and auditors. The honest answer for most teams is monitoring and dashboards — which observe, but don't decide.

Controls decide; monitoring observes

The distinction is the whole game. Monitoring tells you a request was expensive, out of policy, or wrong — after it ran. A control evaluates the request first and can deny, constrain, or throttle it before anything happens.

• Policy controls — which providers, models, and actions are allowed
• Budget controls — spend caps enforced before tokens are consumed
• Scope controls — an agent acts only within what its task permits
• Rate controls — a runaway loop hits the boundary, not your bill

How Keel enforces AI controls

Keel sits in the request path and evaluates a permit before each AI action — a fail-closed decision against the controls you define. Anything outside policy doesn't get a permit and doesn't run. Each decision becomes a tamper-evident, independently verifiable record.

That means your AI controls aren't just configured — they're demonstrable. When someone asks whether a control was in force and what it decided, you have proof a third party can check without trusting Keel.

Why proof matters as much as enforcement

A control you can't demonstrate is a control you can't defend in a review. Keel binds each decision to verifiable evidence, so "we have AI controls" becomes "here is what each control decided, and you can verify it yourself."

Frequently asked questions