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Course

Artificial Intelligence Governance

BasicSkill Level

4.8+

Updated 05/2026

Learn AI governance with Collibra. Build, embed, and scale responsible AI using tools, frameworks, and MLOps workflows.

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TheoryArtificial Intelligence2 hr12 videos51 Exercises3,200 XP4,024Statement of Accomplishment

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Course Description

This course on AI Governance offers a guide to building responsible, scalable governance systems for AI. Featuring insights from experts at Collibra, you’ll learn how to align ethics, compliance, and business goals in real-world AI programs.

Start with the Why and Who

You’ll begin by defining the scope of AI governance and aligning key stakeholders, from legal and risk to data science and business. Then, using tools like readiness assessments and maturity models, you'll learn how to set governance objectives that support both compliance needs and organizational strategy.

Make Governance Work Every Day

Discover how to embed governance into your daily workflows through checklists, approval gates, and automated documentation. Learn to integrate governance into MLOps pipelines and tailor your approach using lightweight or heavyweight models depending on risk and scale.

Scale Smarter Stay Accountable

Explore how to scale governance across teams and regions using federated models and governance platforms like Collibra’s. You’ll also learn to track governance KPIs, maintain traceability, and drive continuous improvement through monitoring and feedback loops.

Prerequisites

There are no prerequisites for this course

1

Foundations of AI Governance

Explore what AI governance is, how it differs from ethics and risk management, and why it’s essential for responsible AI. Learn the key components of governance systems, roles and responsibilities across teams, and how to embed accountability and oversight throughout the AI lifecycle.

What is AI Governance?

Defining AI governance in practice

Distinguishing governance from ethics

Responding to external AI pressures

Who walks the talk?

Components of AI governance systems

Embedding governance across the AI lifecycle

Coordinating roles and responsibilities

Match the parts to their purpose

Governance in motion

Governance roles and responsibilities

Sharing responsibility for AI governance

Roles in AI governance

Collaborating through governance committees

2

Regulations and Frameworks in Practice

Dive into global AI regulations, including the EU AI Act and U.S. Executive Order, and learn how to identify and manage high-risk systems. Explore key governance actions like conformity assessments, model documentation, and impact assessments, and understand how self-regulation and traceability build compliance, trust, and long-term accountability.

3

Implementing Governance in Organizations

Learn how to design, embed, and scale AI governance in real-world settings. This chapter covers stakeholder alignment, workflow integration via MLOps, lightweight vs. heavyweight governance models, automation for scalability, and KPI-based monitoring strategies to drive continuous improvement and accountability across your AI systems.

Designing an AI governance strategy

Foundational steps

Define governance objectives

Assessing organizational readiness

Operationalizing governance workflows

Governance in MLOps workflows

Lightweight versus heavyweight

Checklist items

Governance at scale

Scaling governance across teams

Governance platform capabilities

Why automation matters for scale

Monitoring and continuous improvement

Post-deployment issues

Monitoring governance actions

Governance KPI examples

Collibra in action

Artificial Intelligence Governance

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FAQs

Is this a technical coding course or a conceptual course?

This is a theory-based conceptual course with no coding. It focuses on governance frameworks, regulations, and organizational strategies for responsible AI.

Does the course cover the EU AI Act?

Yes. Chapter 2 dives into global AI regulations including the EU AI Act and U.S. Executive Order, covering conformity assessments, model documentation, and compliance strategies.

Who developed the course content on AI governance?

The course was developed with expert guidance from Collibra, providing practical skills for scaling governance, monitoring with KPIs, and embedding accountability in AI workflows.

What organizational roles benefit most from this course?

Anyone involved in deploying or overseeing AI systems benefits, including data leaders, compliance officers, ML engineers, and managers who need to align teams around governance policies.

How does this course connect governance to MLOps workflows?

Chapter 3 covers embedding governance into MLOps pipelines, choosing between lightweight and heavyweight governance models, and using automation and KPIs for scalability.

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