Monitoring, Drift & Incident Response

AI governance does not end when a model is deployed. Organizations must continuously monitor AI systems, identify performance degradation, detect emerging risks, respond to incidents, and maintain trust throughout operational use. This lesson explores monitoring, drift detection, and incident response within AI governance programs. Learners will examine operational monitoring practices, model drift, data drift, incident classification, root cause analysis, corrective actions, and post-incident governance reviews. Understanding monitoring and incident response is essential for AI governance auditors because ongoing assurance depends on an organization’s ability to identify, investigate, and remediate issues before they create significant operational, regulatory, or reputational harm.