Category: AI Governance

AI governance is the system of policies, roles, controls and records an organisation uses to ensure its AI systems are lawful, accountable and monitored throughout their lifecycle. This section covers the discipline itself, independent of any single law or standard: what governance means in operational terms, how it differs from AI ethics statements and from narrow risk management, and what the working components look like — inventory, classification, risk assessment, control implementation, residual risk tracking, audit trails and board reporting. The articles are aimed at the people accountable for making governance real: compliance officers, DPOs, general counsel, risk leads and the practitioners who must produce evidence when a regulator, auditor, customer or board asks how AI is controlled. The consistent theme is defensibility — governance that survives scrutiny is built on records, ownership and review triggers, not on principles documents. Frameworks including the EU AI Act and ISO/IEC 42001 are referenced throughout, but the focus here is the operating model that underpins all of them.