AI Governance

The operating question

Who owns the use case, the evidence, the release decision and the response when performance changes?

A practical framework

• Classify the task, users, data, decision impact and prohibited uses.
• Define evaluation, approval, access, logging and change controls.
• Monitor real use, incidents and model or source changes against release criteria.

What the technical work needs to resolve

Governance should be proportionate to consequence. A drafting assistant and a system influencing product disposition do not need identical controls, but both need a clear intended use and accountable owner.

Model updates, retrieval-source changes and prompt changes can alter behavior. Release governance should treat them as controlled configuration when the output supports important work.

Scale the framework to what is riding on the decision. A narrow issue may need a short evidence review; a production or quality decision may need controlled requirements, representative trials, traceability and formal disposition. In either case, keep assumptions visible so later changes do not quietly invalidate the conclusion.

Common failure modes

• Publishing principles without operating controls.
• Assigning responsibility to a committee instead of named owners.
• Testing average accuracy while missing high-consequence failure modes.
• Ignoring shadow use and data exposure outside the approved workflow.

These failure modes are tempting because they create visible activity quickly. The risk begins when activity is mistaken for progress. Close the work only when evidence shows that the requirement, process or decision now behaves as intended.

To put the framework into practice, define the owner, controlled inputs, interfaces, expected output, exception path, acceptance criteria and verification record. That turns the insight into something usable in a design review, supplier discussion, production trial or management decision.