AI Risk Register Explainer
An AI risk register is a living record of risks associated with an AI system or portfolio. Each entry typically describes a risk scenario, possible causes and consequences, likelihood, impact, affected assets or groups, and responsible owner.
The register also records existing controls, planned treatment, deadlines, evidence, status, and residual risk after mitigation. Links to incidents, evaluations, vendors, models, datasets, and decisions make the record easier to investigate and update.
Teams use the register to compare priorities and avoid losing issues across meetings or handoffs. A severe but unlikely event may require a different response from a frequent low-impact failure, even when both deserve attention.
The register should change as evidence changes. New evaluations, user feedback, model versions, deployment contexts, and incidents can alter likelihood or impact. Stale entries create false confidence, so review schedules and escalation rules matter.
An AI risk register is a maintained record of identified risks, their likelihood and impact, responsible owners, controls, status, and planned treatment.
Key terms include risk scenario, likelihood, impact, owner, control, treatment, residual risk, status and escalation.
A useful entry says who is responsible and what action is planned.
Without ownership and deadlines, the register becomes a list rather than a control system.
Fresh evidence can change both the probability and severity of a risk.
Regular review keeps priorities aligned with the current model and deployment context.
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