Data Governance for Generative AI

Overview

This course offers a comprehensive exploration of data governance fundamentals critical for managing AI systems. You will learn about establishing AI governance foundations, regulatory compliance, and risk assessment frameworks aimed at ensuring responsible AI usage. Key lessons include implementing ISO/IEC 42001 controls, developing robust model governance, and formulating data retention policies. The course emphasizes the importance of third-party vendor governance and stakeholder engagement. Through a hands-on project, you will create a comprehensive AI governance framework, equipping you with the strategies necessary for effective oversight and management in the generative AI landscape.

Syllabus

Course Introduction

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.

Understanding Data Governance Foundations for Generative AI

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

Establishing AI Governance Foundations

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.

Understanding Regulatory Compliance for AI

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.

Implementing Regulatory Compliance Plans

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.

Understanding AI Risk Assessment Frameworks

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.

Conducting AI Risk Assessments

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.

Understanding ISO/IEC 42001 for AI Management

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

Implementing ISO/IEC 42001 Controls

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

Understanding AI Model Governance

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

Implementing AI Model Governance

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

Understanding Data Retention and Privacy for AI

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

Implementing Data Retention and Deletion Policies

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

Understanding Third-Party AI Vendor Risks

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.

Implementing Third-Party AI Vendor Governance

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.

Understanding AI Governance Frameworks and Stakeholder Engagement

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

Implementing Stakeholder Engagement Processes

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

Understanding AI Incident Management

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

Implementing AI Incident Response

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

Understanding AI Governance Organizational Design

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

Developing AI Governance Policies and Structure

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

Understanding Sovereign AI

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.

Project: Develop a Comprehensive AI Governance Framework

Assess an AI healthcare system's readiness for EU launch by evaluating regulatory compliance, governance risks, vendor oversight, model performance, monitoring, and executive readiness.