Post-Deployment Monitoring
Ongoing observation of AI system behavior and environment after release to detect degradation, drift, or compliance breaches.
Definition
Extends model monitoring to include governance signals—privacy incidents, policy-violation logs, ethics-metric trends—alongside performance and security metrics. Post-deployment monitoring frameworks ingest diverse telemetry, run periodic audits (e.g., fairness checks, anomaly scans), and trigger governance workflows when thresholds are crossed, ensuring corrective actions (retraining, rollback, legal notification) occur promptly.
Real-World Example
A social-media platform’s hate-speech detector sends all content flagged as high-risk or unclassified by the AI into a moderator queue. Post-deployment, the system monitors false-positive rates monthly; if the false-positive rate exceeds 2%, the governance dashboard opens an investigation ticket for model retraining and rule adjustments.
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