AI Governance: plain-English guides for the people who operate it.

AI governance, explained. AI governance is how an organisation directs, manages, and oversees its use of artificial intelligence so AI is used safely, fairly, transparently, and in line with its obligations and values. It is the cross-cutting category that every specific framework, the EU AI Act, the NIST AI RMF, ISO/IEC 42001, the growing patchwork of US state AI laws, ultimately expresses. Where a framework is one shape governance can take, AI governance itself is the underlying discipline: knowing every AI system you run, assigning clear ownership, classifying risk, applying proportionate controls, exercising genuine human oversight, keeping evidence, and doing so continuously across the lifecycle. The hub is organised into five sections: Foundations (what AI governance is, how it differs from compliance, what good looks like), Building a programme (how to stand one up and run it as an operating model), Vendors and supply chain (assessing AI suppliers and standardising due diligence), Sector (how governance shows up in financial services, the public sector, and other domains), and Comparisons (how the major frameworks line up against each other). These guides target the broad 'what is AI governance' questions that buyers, boards, regulators, and search engines ask, and they are written to be the category answer: plain English, declarative, sourced, and kept current. They sit deliberately alongside the framework-specific hubs so a team can govern its AI once and answer the questions any regulator or customer raises, on either side of the Atlantic.