How do I monitor AI risks in production?

Monitoring AI risks in production is essential because AI systems degrade over time as data distributions shift, adversaries adapt, and usage patterns evolve. Effective monitoring combines automated metrics, periodic reviews, and incident response mechanisms to detect and address risks before they cause significant harm.

Performance monitoring tracks model accuracy, precision, recall, and error rates over time. Degradation signals potential drift or data quality issues. Segment performance by demographic groups to detect emerging bias. Latency and throughput metrics ensure operational reliability. Automated alerts trigger when performance falls below acceptable thresholds.

Drift detection identifies changes in data distributions or input-output relationships. Statistical tests compare production data against training data. Concept drift detection monitors whether the relationship between features and predictions has changed. When drift is detected, models may require retraining or recalibration.

Fairness monitoring measures disparate impact across demographic groups. Fairness metrics (demographic parity, equalized odds, calibration) are tracked continuously. Violations trigger reviews to determine root causes and implement corrective actions. Some organizations publish fairness dashboards to demonstrate accountability and transparency.

Adversarial monitoring tests for vulnerabilities through red team exercises and continuous probing. Monitoring detects unusual input patterns that may indicate attacks. Anomaly detection flags inputs that differ significantly from training data, which may be adversarial or out-of-distribution examples the model shouldn't process.

Incident tracking logs AI-related failures, user complaints, and near-misses. Root cause analysis identifies systemic issues versus isolated events. Trends in incidents inform risk reassessments and control updates. Escalation protocols ensure serious incidents reach decision-makers promptly.