AI Governance
Govern the system, not the label.
AI governance is the discipline of knowing what is in use, why it is being used, who is accountable and what evidence supports continued operation.
- InventoryKnow which AI systems are in use, who relies on them and which business processes they affect.
- ImpactAssess consequences, affected people, data use and the conditions under which additional review is required.
- OversightDefine decision rights, review points, escalation paths and evidence before a system enters or remains in use.
- Change and retirementGovern model, vendor and workflow changes through the full lifecycle instead of treating approval as the end of governance.
Governance has to survive contact with operations.
The learning connects governance to security, procurement, data, cloud architecture and the people responsible for day-to-day use. Policies matter only when the operating model can support them.
View the common curriculum