AI governance is the framework an organization puts around AI use: who can deploy AI features, what review processes exist, how risks are assessed, what data can be used for training, how models are monitored in production, and who's accountable when something goes wrong.
For organizations just starting out, governance often means three things: an approved-tools list (which AI services are sanctioned for which data sensitivity levels), a lightweight risk assessment for new AI features (what could go wrong, who's affected, how is it monitored), and incident response (what happens when the AI produces a bad output).
Mature AI governance programs track things like bias testing before deployment, model cards for every internal model, automated monitoring for output quality drift, and regular red-team exercises. Regulation is coming — the EU AI Act, NIST AI RMF, and similar frameworks are increasingly referenced by enterprise customers in procurement questionnaires.
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