AI governance is the record you can show when someone asks.
AI governance is the set of policies, roles, and controls an organization puts in place to develop and use AI responsibly and in line with the law. It covers questions of accountability, transparency, fairness, privacy, and risk management across an AI system's life. With regulations such as the EU AI Act, sound governance helps organizations stay compliant while building trust in their AI.
AI governance is how an organisation decides which AI systems it runs, who is accountable for each, and what evidence exists that they behave as intended.
Most explanations summarise the regulation. That is not the hard part. The hard part is that governance is a set of documents somebody has to keep current, and knowing which ones is worth more than another summary of the law.
1. A model register: every AI system in use, what it decides, what data it touches, and who owns it. Most organisations cannot produce this, and discovering the shadow tools is usually the first real finding. 2. A human-oversight record, for each system, what a person can review, override or stop, and who that person is. 3. A logging policy, what is recorded about each decision and for how long, because "the model decided" is not an answer without a log. 4. An incident procedure, what happens when a system produces a harmful or clearly wrong output, written before it does.
The common failure is writing a governance policy first. A policy written before the register describes systems nobody has enumerated, and is obsolete on contact with the actual estate. Build the inventory first: walk the departments and ask what tools people use to make or support decisions. Expect to find subscriptions IT does not know about. The register makes the policy writable, because you are then governing things that exist rather than things you imagined.
Governance effort should track consequence. A model that drafts internal meeting summaries and one that screens job applicants do not warrant the same scrutiny, and treating them identically guarantees the framework is ignored. Sort by who is affected and how reversible the outcome is: systems touching people's employment, credit, health or legal standing get the full treatment; internal productivity tools get a register entry and an owner. This is also the logic the EU AI Act uses, so a proportionate internal approach maps onto it reasonably well.
A spreadsheet and a named owner beats an unimplemented framework. The realistic first version is: one sheet listing every AI tool with an owner and a one-line description of what it decides, a short note per system on what a human can override, and a decision about what gets logged. That is perhaps a day of work and it puts you ahead of most mid-sized companies. Governance is a habit, not a document, the register only has value if something makes it get reviewed.
No. Compliance is a legal obligation with a defined scope; governance is the internal practice that makes compliance demonstrable and also covers systems the regulation does not reach.
Someone with authority across departments, not the AI team. The AI team cannot meaningfully govern itself, and IT often lacks visibility into tools bought on departmental cards.
Enough to reconstruct why a specific output happened, months later, without the original engineer.
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