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

Foundations of AI Governance

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.

Key regulatory frameworks

Understanding regulatory reach

Core elements of high-risk AI regulation

High-risk AI in healthcare

Governance requirements by risk

Credit scoring: compliance order

Self-regulation versus government oversight

In-game governance

Voluntary standards: match the framework

Proactive versus reactive

Governance by sector

Applying governance requirements to systems

Hiring risk

Legal actions required

Risk classification

AI compliance in practice

Governance documentation and traceability

Model cards

Governance artifacts

From training to traceability

Regulations and Frameworks in Practice

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

Options

Business goals

Compliance goals

Define governance objectives

Assessing organizational readiness

Operationalizing governance workflows

Governance in MLOps workflows

Lightweight Governance

Heavyweight Governance

Lightweight versus heavyweight

Checklist items

Governance at scale

Scaling governance across teams

Automation

Documentation

Governance platform capabilities

Why automation matters for scale

Monitoring and continuous improvement

Post-deployment issues

Model/Development Review

Policy Refinement

Monitoring governance actions

Governance KPI examples

Collibra in action

Implementing Governance in Organizations

Want to ensure your AI systems are ethical, scalable, and compliant? This course gives you the tools to make that happen. You'll learn how to align teams, build governance into MLOps workflows, and apply frameworks like the EU AI Act. With expert guidance from Collibra, you’ll gain the skills to scale governance across your organization and monitor its success through automation, KPIs, and continuous feedback.

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.<br><br>
<h2>Start with the Why and Who</h2>
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.<br><br>
<h2>Make Governance Work Every Day</h2>
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.<br><br>
<h2>Scale Smarter Stay Accountable</h2>
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.


Learn AI governance with Collibra experts. Design, embed, and scale responsible AI using real-world frameworks, compliance tools, and MLOps workflows.

Artificial Intelligence Governance

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

Fundamentos de la IA responsable

Unexpected model bias observed in production outcomes

Documentation for a deployed model was found to be incomplete

Approval delays are significantly impacting model rollout

Performance audit shows unclear fairness thresholds across use cases

Monitoring governance actions

Artificial Intelligence Governance

Ejercicio interactivo práctico