Data Governance for Generative AI

Get introduced to AI governance for generative models, course outcomes, prerequisites, and tooling to prepare for applied hands-on learning and a real-world capstone project.

Learn how data governance for Generative AI demands new frameworks, skills, and dynamic, cross-functional risk management beyond traditional approaches.

Learn to build AI governance foundations: draft a charter, map stakeholders, design a communication plan, and structure committee roles with FATE-aligned oversight for GenAI systems.

Explore global AI regulations, EU AI Act risk tiers, and how to use standards like ISO/IEC 42001 and NIST AI RMF to build scalable, cross-border AI compliance programs.

Learn to operationalize AI regulatory compliance by building a compliance matrix and Python risk classifier, mapping system risks to EU AI Act requirements, evidence, and remediation actions.

Learn to assess and manage AI risks using ISO 31000 and NIST AI RMF, focusing on technical, ethical, legal, and operational risks, prioritization, and real-world mitigation techniques.

Learn to conduct AI risk assessments by building risk registers, scoring and visualizing risks, and generating executive-ready reports using real healthcare and fintech scenarios.

Explore ISO/IEC 42001 and how it enables organizations to structure, evaluate, and certify responsible AI governance using a robust, integrated management system.

Learn to implement ISO/IEC 42001 controls by conducting gap analysis, automating compliance reporting, and using visualizations for effective AI governance and certification readiness.

Learn comprehensive AI model governance, including lifecycle stages, registries, risk tiering, documentation standards, approval workflows, and why governance is essential for GenAI systems.

Build automated AI model governance tools: generate Model Cards, track lifecycle, check approval gates, and create dashboards for scalable, auditable, and repeatable compliance.

Explore AI's data retention paradox, deletion challenges post-training, regulatory conflicts, key data types, and privacy-preserving techniques for responsible AI governance.

Learn to create automated, auditable data retention and deletion workflows, select proper deletion methods, assess model retraining needs, and ensure regulatory compliance.

Learn to identify, categorize, and mitigate the unique risks of third-party AI vendors, including data, bias, drift, lock-in, and security with robust governance and contracts.

Learn to assess and govern third-party AI vendors using risk scoring, SLA compliance dashboards, visual comparisons, and actionable recommendation tools for robust governance decisions.

Learn to design effective AI governance frameworks using five key pillars, choose the right operating model, and engage stakeholders for legitimacy and adoption.

Learn to build structured stakeholder engagement plans, impact assessments, advisory board charters, and feedback mechanisms to operationalize AI governance for vulnerable populations.

Understand unique AI incident types, detection strategies, response frameworks, severity levels, blameless learning, and emerging regulatory requirements for effective AI incident management.

Learn to operationalize AI incident response: build playbooks, severity matrices, detection and escalation logic, and reporting workflows for safety-critical AI systems.

Explore AI governance organizational design: compare governance models, use RACI for roles, establish effective ethics boards, define clear policies, and ensure accountability and escalation.

Learn to design and implement comprehensive AI governance: create enforceable use policies, RACI matrices, ethics board charters, and robust review workflows for regulatory compliance.

Understand sovereign AI: how organizations govern AI systems using FATE principles, a tiering framework, and practices like Regulatory as Code and deep literacy to align with their values and laws.