AI governance
What is AI governance?
The structures that keep AI use controlled and accountable: who decides, what is allowed, how models get monitored across their life. It is what regulators and boards ask about first. Frameworks like NIST's AI RMF and ISO/IEC 42001 give it shape, but governance is people and process before it is any tool.
Why it matters
Skip governance and you do not avoid the work, you just pay for it later and in public. The failure is quiet at first. Different teams pick their own tools, nobody can say which models touch customer data, and no single person is accountable when one produces a bad decision. Then something goes wrong and the board discovers there was never an answer to a basic question: who approved this. Governance gets mistaken for a brake. Done well it is closer to the opposite, a clear set of rules that lets teams move faster because they know in advance what is allowed and what is not.
In practice
A board asks a plain question after a model misfires: who signed off on using it, and against what criteria. If the honest answer is that nobody did, that gap is the governance problem, not the model. The fix is not complicated. Name an owner for each significant AI use, write down what approval requires, and keep a record that outlives the people who leave.
