Building an AI risk register that teams will use

Write risks as observable scenarios

Describe each risk as a concrete event with a trigger, consequence, owner and response. Separate likelihood judgments from impact and note the evidence behind both. A useful register supports action; a generic list of hallucination, privacy and cost does not explain what anyone should do when a problem occurs.

Worked example

Illustrative scenario; not a claim of measured client results.

For a knowledge assistant, a risk could be answering from a superseded policy after the source changes. The trigger is an update that fails to reach the index; the response includes pausing affected answers, refreshing the source and checking recent outputs. Assign a person who can execute that response and define how the failure would be detected.

Checks, limitations and next decisions

Review risks after evaluations, incidents and scope changes. Close or revise items when controls change, rather than accumulating a permanent list. Include operational failures such as provider outages and duplicate actions alongside model errors. Test the response for high-consequence scenarios so the register becomes part of the operating workflow rather than a document prepared only for approval.

A practical starting point

Work through these checks in order. Use the results to decide what needs to change in your own workflow.

1. Describe concrete failure scenarios in the workflow.
2. Assign an owner and a control for each significant scenario.
3. Review incidents and near misses to update priorities.

A mistake to avoid

Do not mark every issue critical or leave controls without responsible people.

How to check progress

Track open controls and whether tested safeguards actually prevent the described failure.