Data Governance & AI Governance: The Complete Guide

<div>Your organization is deploying AI. Data teams are growing. Regulations are tightening. And someone has to make sure the data can be trusted and the models can be explained. That's what this course is about.</div><div><br></div><div>Data Governance has expanded well beyond traditional data management and this course reflects that reality.</div><div><br></div><div>The course is built in two halves. The first builds your core governance program: operating model, policies and controls, business glossary, data quality, privacy, and lineage. The second covers AI governance: assessing and documenting AI risk, governing ML pipelines, and writing GenAI-specific policies for RAG ingestion, prompt safety, and response governance.</div><div><br></div><div>The labs are real governance work. Every module has a hands-on assignment built on EasyCar,&nbsp; a realistic car rental case study that runs through the entire course. You'll draft business glossary terms with ownership and exclusions, write testable data quality rules with severity levels, produce an AI Impact Assessment, fill in a model card and data card, design a human oversight plan for a fraud detection model, and define GenAI prompt logging standards.</div><div><br></div><div>What makes this course different:</div><div><ul><li><span style="font-size: 1rem;">End-to-end scope: From writing your first governance charter to governing ML pipelines, RAG corpora, and LLM prompt safety</span></li><li><span style="font-size: 1rem;">14 hands-on lab assignments: One per module, all built on the same EasyCar case study that runs continuously from Module 1 to Module 14, so context and decisions compound as you progress rather than starting over each time</span></li><li><span style="font-size: 1rem;">15+ ready-to-use templates included: charters, RACI matrices, policy catalogs, model cards, AI risk registers, data cards, incident runbooks, and more — ready to adapt and deploy</span></li><li><span style="font-size: 1rem;">Regulatory alignment: Modules map to EU AI Act, NIST AI RMF, ISO/IEC 42001, and GDPR so you know where your controls map to real obligations</span></li><li><span style="font-size: 1rem;">Vendor-agnostic: All principles and templates work regardless of your tooling stack</span></li></ul></div><div><span style="font-size: 1rem;">You'll finish with a governance charter, a policy catalog, a data quality rulebook, an AI risk register, a model card, a data card, and a GenAI policy; artifacts you can put in front of your organization immediately.</span></div>

What you'll learn:

• Design a data governance operating model (federated, centralized, or hub-and-spoke) with a RACI, council charter, and decision rights
• Write policies, standards, and controls across the full policy hierarchy — corporate to system level
• Build a business glossary and metadata catalog with naming standards and curation workflows
• Define data quality rules, thresholds, and SLAs; manage issues with ownership and remediation workflows
• Apply data classification, access governance, DPIA/PIA, and retention standards to meet GDPR and privacy requirements
• Build an AI governance program: impact assessments, model cards, data cards, risk registers, and human oversight plans
• Govern the ML lifecycle and GenAI deployments: approval gates, RAG corpus policies, prompt safety, and hallucination evaluation frameworks
• Walk away with 15+ production-ready templates you can adapt and deploy immediately