AI governance

AI governance is the set of policies, processes, controls and accountability structures through which an organisation manages how AI is developed, deployed and used. Its purpose is to ensure that AI systems operate within the organisation’s legal, regulatory and ethical obligations, that risks are identified and managed, and that there is clear ownership of decisions rather than diffuse responsibility for a system nobody controls.

In practice, governance spans the lifecycle of an AI system: deciding where AI is and is not appropriate, documenting data boundaries and how data is handled, defining access controls and human oversight, keeping records and audit trails that a risk or compliance function can evidence, monitoring systems in production, and maintaining accountability for outcomes. In regulated sectors, AI does not receive lighter treatment because it is new — the same expectations around data handling, model risk, resilience and accountability apply as to any other critical system.

Good governance is a design input, not a document produced after the fact. Building deployments with documented boundaries, evidenced controls and appropriate human oversight from the outset is what allows AI to be adopted in sensitive environments at all — it is frequently the difference between a programme an organisation’s risk function will approve and one it will block.