AI Governance

The accountability structure that decides who can approve, deploy, and stop an AI system — the discipline that makes every other compliance effort actually hold up under pressure.

AI governance is what determines whether a company's compliance program is real or decorative. Risk management identifies and mitigates specific risks in a specific system; governance is one level above that — it's the decision rights, review processes, and named accountability that determine who has the authority to say a risk is acceptable, and who gets to stop a deployment before it happens.

A governance framework that can't name who has the authority to say no to a specific system isn't a governance framework yet, regardless of what the policy document calls it. This hub covers the structural pieces: inventory, risk tiering, accountable ownership, and review gates placed early enough to actually change an outcome.

Featured

Most AI governance frameworks fail for the same reason: they're written to look complete in a slide deck, not to survive contact with a real model deployment. Here's what to build first, in what order, and why the sequence matters more than the paperwork.
Governome Editorial Team · 4 min read

Recently updated

Board-level AI oversight usually fails in one of two directions: no oversight at all, or oversight so generic it doesn't change what management does. Here's what directors should actually be asking.
Governome Editorial Team · 3 min read
Most AI governance committees fail for the same reason most committees fail: no real decision authority, no clear charter, and no named accountability when something goes wrong. Here's what a working one actually looks like.
Governome Editorial Team · 3 min read
Most AI governance frameworks fail for the same reason: they're written to look complete in a slide deck, not to survive contact with a real model deployment. Here's what to build first, in what order, and why the sequence matters more than the paperwork.
Governome Editorial Team · 4 min read

Frequently asked questions

What is AI governance, in practical terms?
The set of decision rights, review processes, and accountability structures that determine how an organization builds, buys, and deploys AI systems — and who is responsible when one of them fails. See our full governance framework checklist for how to build this in the order that tends to hold up.
Who should own AI governance at a company?
For consequential systems, someone whose job description would credibly include "explain this system's behavior to a regulator" — usually a risk or product owner close enough to the system to actually understand it, and senior enough to stop its deployment. A committee alone rarely works, because committees can't be held individually accountable the way a named owner can.
How is AI governance different from AI risk management?
Risk management is about identifying and mitigating specific risks in a specific system. Governance is about who has the authority to decide a risk is acceptable in the first place, and at what point in the deployment process that decision gets made — the structural layer risk management operates inside of.

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