This course provides a structured, practitioner-focused approach to identifying, managing, and governing risks in AI systems across their lifecycle. It equips learners with the tools to move beyond model performance and address real-world concerns such as bias, model degradation, regulatory exposure, and operational accountability.

Learners begin by diagnosing bias in datasets and models, applying fairness metrics, and conducting audits that reveal hidden disparities across demographic groups. The course then advances to bias mitigation, where participants explore practical techniques across the model pipeline and learn to navigate trade-offs between fairness and performance. The course expands into production environments, teaching how to design monitoring pipelines that detect data drift, concept drift, and performance degradation before they impact business outcomes. Learners connect these monitoring signals to structured risk evaluation frameworks, translating technical anomalies into enterprise risk language using scoring models, risk registers, and response strategies aligned with standards such as ISO 31000 and COSO ERM. Finally, the course integrates AI systems into broader governance and compliance structures. Participants learn to map AI use cases to regulatory obligations (e.g., GDPR, EU AI Act), build compliance inventories, and design governance dashboards that support audit readiness and executive oversight. By the end of the course, learners will be able to operationalize AI risk management, implement continuous monitoring, prioritize and respond to model risks, and align AI systems with organizational and regulatory expectations.