Overview

AI, Data Science & Cloud Certificates from Google, IBM & Meta — 50% Off

One plan covers every Professional Certificate on Coursera. 50% off Coursera Plus Annual for 10 days only — price increases June 17.

Unlock All Certificates

This course introduces the foundations and practical implementation of AI governance, helping organizations design and manage responsible AI systems. You’ll begin by understanding core governance concepts, stakeholder roles, and how governance differs from ethics and compliance. The course then explores global frameworks such as the EU AI Act and NIST AI RMF, enabling you to align AI systems with regulatory expectations. Next, you’ll learn how to operationalize governance through policy design, maturity models, and lifecycle risk management using tools like risk registers and impact assessments. The course also covers monitoring, auditing, and incident response to ensure continuous oversight of AI systems. By the end of this course, you will be able to: - Explain AI governance fundamentals and stakeholder roles - Apply global frameworks to real-world AI systems - Design policies and manage lifecycle risks - Monitor, audit, and respond to AI risks Designed for professionals, analysts, and anyone working with AI systems, this course provides a structured approach to implementing AI governance in practice. To be successful, learners should have a basic understanding of AI concepts and business processes. Start your journey into responsible AI and learn how to build governance systems that ensure accountability and trust.

Syllabus

Global Frameworks and Regulatory Strategy

Explore how AI governance is structured within modern enterprises and aligned with global regulatory expectations. This module covers foundational governance concepts, stakeholder role design, and leading frameworks like the EU AI Act, NIST AI RMF, and ISO standards, enabling structured, compliant, and risk-aware AI system management.

Operationalizing the Risk and Policy Lifecycle

Learn how to translate AI governance into operational practice through structured risk and policy lifecycle management. This module explores maturity models, policy design, supply chain risk, and lifecycle governance, while enabling systematic risk identification, impact assessment, and control implementation using tools like risk registers and AIA frameworks.

Technical Assurance, Oversight, and Crisis Remediation

Examine how AI governance is enforced through continuous monitoring, auditing, and human oversight, while preparing organizations to manage emerging risks and critical incidents. This module focuses on assurance systems, red-teaming practices, incident response strategies, and integration with enterprise GRC to ensure resilient, accountable, and well-governed AI operations.

Course Wrap-Up and Assessments

Synthesize the complete AI governance journey by connecting strategy, operations, and technical assurance into a unified framework. This module brings together governance design, regulatory alignment, risk lifecycle management, and assurance mechanisms to provide a holistic view of how organizations deploy, manage, and scale responsible AI systems in real-world environments.