Govern Your GenAI Data Safely

Overview

The explosion of generative AI has created unprecedented data governance challenges that traditional approaches can't handle. This course equips you with the specialized skills to govern GenAI data safely while maintaining operational agility. This Short Course was created to help machine learning and AI professionals accomplish secure, compliant GenAI data governance at enterprise scale. By completing this course, you'll be able to design sophisticated role-based access control systems, assess your organization's governance maturity using industry frameworks like DAMA-DMBOK, and create comprehensive stewardship programs that balance innovation with security. These are the foundational skills that separate GenAI operations that scale safely from those that create compliance nightmares. By the end of this course, you will be able to: - Analyze data access patterns across user cohorts to recommend precise role-based controls - Evaluate governance maturity using established frameworks to identify strategic improvement opportunities - Create data stewardship programs with clear ownership, quality standards, and governance procedures This course is unique because it bridges the gap between cutting-edge GenAI capabilities and enterprise-grade governance, focusing specifically on the intersection of AI operations and data security. To be successful in this project, you should have experience with data analytics, understanding of enterprise risk concepts, and familiarity with AI/ML environments.

Syllabus

Module 1: Analyze Data Access Patterns for RBAC Recommendations

This module establishes critical skills for securing GenAI data through precise access controls. Learners explore why traditional permission models fail in AI environments, master access pattern analysis and function-based RBAC design, and gain hands-on experience using SQL techniques to analyze real access logs.

Module 2: Evaluate Governance Maturity Against Frameworks

This module transforms learners from framework consumers to assessment practitioners who can lead organizational governance improvement initiatives. Learners master DAMA-DMBOK components and advanced assessment techniques, then practice facilitating maturity workshops through screencast demonstrations.

Module 3: Create Comprehensive Data Stewardship Programs

This module integrates course concepts into practical stewardship program design capabilities. Learners master the five essential components of effective programs—ownership assignment, quality frameworks, and governance procedures—then develop complete documentation and design skills to transform organizational data governance from ad-hoc practices into systematic capabilities that enable secure, compliant GenAI operations.