Govern Your GenAI Data Safely

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Govern Your GenAI Data Safely

This course is part of multiple programs.

Instructor: Hurix Digital

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Effective RBAC uses real usage patterns, not assumptions, to ensure access controls match actual workflows and security needs.
Governance maturity assessment with frameworks like DAMA-DMBOK provides benchmarks to guide progress and investment decisions.
Sustainable data stewardship succeeds with clear ownership, quality standards, and documented procedures that enable accountability .
GenAI data governance balances rapid innovation with enterprise security and compliance requirements for responsible adoption .

Skills you'll gain

Tools you'll learn

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There are 3 modules in this course

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.

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.

What's included

3 videos1 reading2 assignments

3 videos Total 14 minutes

Why Precise RBAC Matters in GenAI Operations 3 minutes
RBAC Design Principles for Secure AI Data Access 5 minutes
Analyzing Query Logs to Identify Access Pattern Anomalies 6 minutes

1 reading Total 10 minutes

Fundamentals of Access Pattern Analysis for AI Systems 10 minutes

2 assignments Total 20 minutes

Hands-On Access Pattern Analysis for RBAC Optimization 15 minutes
Access Pattern Analysis and RBAC Design - Knowledge Check 5 minutes

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.

What's included

2 videos2 readings1 assignment

2 videos Total 11 minutes

Maturity Assessment Techniques for AI Governance Programs 7 minutes
Conducting DAMA-DMBOK Maturity Assessment Workshops 5 minutes

2 readings Total 20 minutes

DAMA-DMBOK Framework for AI Data Governance Assessment 10 minutes
Strategic Gap Identification and Improvement Prioritization Methods 10 minutes

1 assignment Total 3 minutes

Governance Framework Assessment and Strategic Planning - Knowledge Check 3 minutes

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.

What's included

2 videos1 reading3 assignments

2 videos Total 16 minutes

Essential Components of Data Stewardship Programs for AI Environments 11 minutes
Building Data Stewardship Program Documentation and RACI Matrices 5 minutes

1 reading Total 10 minutes

The Strategic Impact of Data Stewardship on AI Operations Success 10 minutes

3 assignments Total 35 minutes

Complete Data Stewardship Program Design and Implementation Planning 15 minutes
Data Stewardship Program Design Fundamentals 5 minutes
GenAI Data Governance and Stewardship - Assessment 15 minutes

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Instructor

Hurix Digital

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