AI governance is the system of policies, responsibilities, controls, and processes ensuring AI is used safely, ethically, legally, and effectively in an enterprise. The post argues that AI governance is inseparable from data governance — you cannot govern AI without governing the data behind it. It outlines seven pillars of effective AI governance: AI inventory, clear accountability, risk-based classification, governed data and context, testing and validation, continuous monitoring, and evidence/auditability. Practical steps for building a programme are provided, along with common mistakes such as separating AI governance from data governance, treating governance as a one-time compliance exercise, or assigning responsibility without authority. The post also promotes Decube's data cataloguing and lineage platform as a foundation for AI governance.
