What does AI Governance mean?

AI governance describes an organisation’s binding rules for selecting, deploying, and monitoring AI systems. It sets out who decides on a deployment, who is responsible for it during operation, and what triggers a shutdown. Unlike a guideline, it names responsibilities and prescribes procedures instead of formulating intentions.

At its core is a register of all systems in use, listing purpose, data basis, model version, place of operation, and the responsible person. Every process is assigned a risk level that determines how much review it needs, and it passes through a defined approval path before going live. In operation, three controls apply: monitoring for model drift, regular review of error rates, and a reporting channel for anomalies. Any change to the model, its version, or its data source triggers a new approval.

AI governance pays off as soon as more than one process is in use and different departments run their own initiatives. It settles once, up front, the questions that would otherwise be renegotiated in every project: who approves, who reviews, who is liable in a dispute, and who switches a system off in case of doubt. For a single, clearly bounded process, a process description is enough on its own.

Its advantage over case-by-case decisions lies in comparability. All initiatives go through the same steps, so effort and risk can be weighed against each other and duplicate work between departments becomes visible. When a regulator reviews the organisation, the evidence is already at hand instead of being assembled afterwards from project folders.

The EU AI Act mandates such a register for high-risk systems and requires information on data, human oversight, and robustness. Meeting that requirement is only possible with access to model version, data basis, and logs, meaning information that must be contractually guaranteed for any purchased service.