Governance & compliance
As AI spreads across a company, governance is what keeps it auditable and accountable. Its absence is what turns a promising pilot into an unmanaged risk.
In plain terms
Model governance is the set of policies and controls for approving, monitoring, and documenting the AI models an organization uses.
Model governance is the short list of rules for how a model is chosen, changed, watched, and retired. It names an owner, the data it may see, the tests it must pass, and who can approve a change. Without it, every team runs a different model under a different personal account.
Keep the rules lighter than a framework poster. One page can be enough: owner, purpose, data allowed, test set, review rhythm, and how you turn it off. The failure is not a missing policy document. It is a model in production that nobody can name, on data nobody approved.
Think of it this way: Model governance is the change management process applied to AI. You would not roll out new enterprise software with no documentation, no approvals, and no rollback plan. AI models deserve the same rigor.
A bank runs 14 AI models across credit, fraud, and marketing. Model governance ensures every model has a named owner, a documented version, a performance benchmark, and a review schedule. When one drifts, the process knows who to call.
Three teams call three models on customer text. Finance cannot see the total bill. Security cannot see the data flows. The company makes one rule: a new model use has an owner, a data note, and a spend line, or it does not launch. The existing three are registered in a month. New ones go through the same gate.
As soon as more than one AI model is in production. The cost of governance at that scale is low. The cost of ungoverned AI at scale, when a model makes a systematic mistake, is high. Model governance should not slow down AI experimentation in the PoC phase. Apply it to production systems, not every internal demo or prototype still being evaluated.
RaftLabs writes the control into the system: which data can enter, who approves the result, and how you explain it later. The rule and the product stay the same story. The related work on our side is AI governance.
This sits with the other governance & compliance terms on the glossary. The rules that keep AI legal, and keep customer data out of the wrong tool. Worth reading next: Data Privacy / PII, GDPR & Compliance, and Shadow AI.
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