Purpose
Ensure every deployed AI system remains safe, performant, and compliant over time by defining uniform monitoring expectations, escalation paths, and retraining triggers.
Scope
Applies to all AI services in production, including online inference APIs, scheduled batch jobs, embedded vendor models, and prompt-based assistants exposed to employees or customers.
- Customer-facing AI features (chatbots, recommendation engines, scoring services)
- Internal automation, risk, and decision-support models
- Shadow or canary deployments used to evaluate future releases
- Vendor-provided AI where we own observability responsibilities
Definitions
- Key Performance Indicator (KPI): Quantitative metric that reflects business value (e.g., accuracy, conversion rate, SLA latency).
- Guardrail Metric: Safety or compliance indicator (bias score, toxicity rate, hallucination threshold).
- Drift: Statistically significant deviation in data, predictions, or KPI trends compared to the validated baseline.
Policy
All production AI systems must have active monitoring coverage across business KPIs, technical metrics, data quality, safety guardrails, and infrastructure health. Alert thresholds must be documented before launch, and any sustained breach triggers the incident workflow or rollback. Monitoring evidence must be retained for regulatory inspections.
Roles and Responsibilities
Model Ops Lead configures dashboards and SLOs. Responsible AI team defines safety guardrails and bias thresholds. Product Owner validates KPI definitions and accepts remediation plans. Site Reliability Engineering (SRE) manages infrastructure alerts and coordinates rollback execution.
Procedures
Monitoring setup must include the following steps:
- Instrument: Attach logging and metrics collectors for inputs, outputs, and metadata (model version, feature vector, inference latency).
- Baseline: Capture launch baselines and acceptable ranges for each KPI/guardrail.
- Alerting: Implement multi-level alerts (warning, critical) routed to PagerDuty/Slack with documented playbooks.
- Drift detection: Schedule statistical drift tests on data and predictions; log results in the model inventory.
- Retraining triggers: Define conditions that require model retraining, prompt updates, or configuration changes.
- Post-incident review: Document root cause, corrective actions, and monitoring improvements after every incident.
Exceptions
Monitoring coverage reductions require approval from the Model Ops Lead and Responsible AI. Compensating controls (manual sampling, shortened release cycles) must be defined and tracked with an expiration date.
Review Cadence
Dashboards and alert thresholds are reviewed quarterly, or sooner if models experience repeated incidents. Monitoring effectiveness metrics (mean time to detect, false positives, number of rollbacks) are shared with the governance council.
References
- EU AI Act Article 61 (Post-market monitoring)
- ISO/IEC 42001:2023 Clauses 8.6 and 9 (Operational control and performance evaluation)
- Internal documents: Monitoring Playbook, Incident Response for AI Systems Policy, Retraining SOP