Monitoring is most useful when teams know what observations require action. Thresholds turn broad expectations such as reliable, appropriate, or controlled operation into specific signals that can be reviewed and escalated.
Start with the use case and its failure modes
Choose measures that reflect what could go wrong in the actual workflow. Depending on context, useful signals may include error rates, invalid outputs, override volume, exception volume, latency, unexpected tool use, complaint patterns, data-quality failures, or material shifts in evaluation results.
Define more than one response level
Not every signal needs an immediate shutdown. Set levels for observation, investigation, temporary restriction, human review, rollback, or suspension. Include who has authority at each level.
Account for normal variation
Use representative baseline information and avoid thresholds so sensitive that routine variation produces constant alerts. Review thresholds when the use case, model, data, users, or business process changes.
Link thresholds to evidence
Record the measure, source, calculation, owner, response, and review history. Monitoring should create an auditable path from an observed signal to the action taken.
Do not encode professional standards casually
Thresholds affecting legal, clinical, financial, employment, safety, privacy, accessibility, or other regulated judgments must be established with appropriately qualified owners.
Related knowledge
Use this with AI Model Change Reviews, AI Risk Acceptance Records, AI Governance Control Owners, and AI Human Oversight Plans.
References
This resource supports operational governance. It does not establish legal compliance or replace qualified legal, regulatory, clinical, financial, employment, safety, privacy, accessibility, or other professional review.