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IAPP AIGP Complete Training – AI Governance Mastery

8 days left! Grow your skills with Coursera Plus for $239/year (usually $399). Save now.

IAPP AIGP Complete Training – AI Governance Mastery

Instructor: Packt - Course Instructors

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7 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

7 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Understand the principles of responsible AI governance and apply frameworks that ensure transparency, accountability, & ethical AI system deployment.
Analyze global AI regulations, including the EU AI Act and international standards, to design compliant AI governance programs.
Conduct AI risk assessments and implement lifecycle governance controls to manage security, bias, operational risks, and societal impact.
Develop organizational AI governance structures, policies, and monitoring systems to ensure responsible AI development and deployment.

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There are 7 modules in this course

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Artificial Intelligence is transforming industries, but without proper governance it can introduce serious risks, ethical challenges, and regulatory consequences. In this course, you will build a strong foundation in AI governance and learn how organizations design responsible AI frameworks that balance innovation with accountability. You will explore the principles of responsible AI, risk management strategies, and governance structures that help organizations deploy AI systems safely and ethically. The course begins by introducing the AIGP certification, its exam structure, and the core objectives required to succeed. You will then explore the foundations of AI governance, including AI types, governance principles, organizational roles, and lifecycle-based governance strategies. Through real-world examples, you will understand how organizations establish governance teams, develop policies, and embed ethical oversight into AI systems. As the course progresses, you will examine global AI regulations, standards, and frameworks such as the EU AI Act, OECD AI Principles, and NIST AI Risk Management Framework. You will also explore key legal responsibilities, data protection requirements, intellectual property challenges, and risk classification models used to manage AI systems responsibly. This course is ideal for privacy professionals, compliance officers, AI practitioners, technology leaders, and policy specialists seeking to understand AI governance and prepare for the AIGP certification. A basic understanding of AI concepts, data governance, or technology management is helpful but not required. The course is designed at an intermediate level for professionals looking to develop governance expertise in AI systems. By the end of the course, you will be able to design AI governance frameworks, interpret global AI regulations, implement AI risk management processes, and evaluate AI systems for responsible and compliant deployment.

In this module, we will introduce the Artificial Intelligence Governance Professional (AIGP) certification and its relevance in today’s AI governance landscape. We will explore the structure of the AIGP exam and the knowledge domains covered in the certification. We will also review the course format and learning objectives to help you prepare effectively for the certification journey.

What's included

4 videos1 reading

In this module, we will explore the foundational concepts behind artificial intelligence and why governance is essential for responsible AI adoption. We will examine AI risks, ethical principles, governance roles, and organizational responsibilities across the AI lifecycle. Through practical examples and case studies, learners will gain insight into building effective AI governance frameworks.

What's included

15 videos1 assignment

15 videosTotal 50 minutes

What Is AI and Why Governance Matters3 minutes
Understanding Types of AI and Learning Methods3 minutes
Understanding AI Risks and Societal Harms3 minutes
What Makes AI Different from Traditional Software3 minutes
Core Principles Guiding Responsible AI3 minutes
Defining Key Roles in AI Governance3 minutes
Organizing Effective AI Governance Teams3 minutes
Building a Culture of AI Governance Awareness3 minutes
Adapting AI Governance to Fit Organizational Needs3 minutes
Clarifying Roles Across the AI Lifecycle3 minutes
Applying Governance Across the AI Lifecycle4 minutes
Evolving Privacy and Security Policies for AI Systems4 minutes
Governance of AI Vendors and Supply Chains4 minutes
Case Study: How Telstra Built a Responsible AI Program4 minutes
Case Study: Baidu's AI Principles in a High-Risk Ecosystem4 minutes

1 assignmentTotal 15 minutes

Domain I — Understanding the Foundations of AI Governance - Assessment15 minutes

In this module, we will examine the laws, regulations, and global standards that shape responsible AI governance. We will explore major frameworks such as the EU AI Act, NIST AI Risk Management Framework, and international AI ethics guidelines. Learners will gain clarity on compliance requirements and how organizations align their AI systems with global governance standards.

What's included

21 videos1 assignment

21 videosTotal 90 minutes

Understanding Privacy Laws in the AI Context4 minutes
Consent and Transparency Challenges in AI4 minutes
Core Legal Duties of AI Data Controllers5 minutes
Governing AI Use of Sensitive Personal Data4 minutes
IP Challenges in the Age of AI Systems4 minutes
Anti-Discrimination Laws and AI Accountability4 minutes
AI and Consumer Protection Laws4 minutes
AI and Evolving Product Liability Laws4 minutes
Understanding the Scope and Structure of the EU AI Act4 minutes
How the EU AI Act Categorizes AI by Risk4 minutes
Compliance Obligations Based on AI Risk Tier5 minutes
Enforcement of the EU AI Act and Penalty Framework4 minutes
Legal Responsibilities Based on Actor Roles5 minutes
OECD AI Principles and International Guidelines5 minutes
The NIST AI Risk Management Framework (RMF)4 minutes
ISO/IEC 42001: AI Management System Standard4 minutes
UNESCO and Global Ethical Declarations on AI5 minutes
Understanding NIST ARIA for AI Risk Profiling3 minutes
Establishing Consistent AI Terminology4 minutes
Industry-Specific and Global AI Standards5 minutes
Summary of AI Laws, Standards, and Frameworks2 minutes

1 assignmentTotal 15 minutes

Domain 2 - Module 2: Laws, Standards, and Frameworks for AI - Assessment15 minutes

In this module, we will explore how organizations identify, assess, and manage risks associated with AI systems. We will review governance processes including documentation, oversight structures, and lifecycle risk management practices. Learners will understand how to maintain accountability, transparency, and continuous monitoring for AI systems.

What's included

21 videos1 assignment

21 videosTotal 77 minutes

Defining Risk and Harm in AI Governance3 minutes
AI Risk Assessment and Classification Models5 minutes
Building Risk Mitigation and Control Strategies for AI4 minutes
Enabling Redress and Oversight in AI Systems4 minutes
Structuring Documentation for AI Risk and Governance4 minutes
Structuring AI Governance Boards and Collaborative Teams4 minutes
Case Study: IBM's Model Risk Governance Framework4 minutes
Operational and Security Controls in AI Model Development4 minutes
Embedding Risk Management into AI Model Design4 minutes
Data Governance Foundations in AI Development4 minutes
Tracking Data Lineage and Provenance in AI Systems4 minutes
Structuring AI Training and Testing for Governance4 minutes
Handling Errors and Anomalies in AI Model Development4 minutes
Capturing and Managing AI Training and Testing Documentation3 minutes
Evaluating AI Systems for Release and Disclosure4 minutes
Monitoring AI Systems Post-Deployment3 minutes
Auditing and Stress Testing AI Systems for Robustness3 minutes
Managing AI Incidents and Failures Responsibly3 minutes
Enabling Ongoing Stakeholder Collaboration After AI3 minutes
Ensuring AI Transparency and Regulatory Disclosures3 minutes
Summary of AI Risk and Accountability Governance3 minutes

1 assignmentTotal 15 minutes

Domain 3 - Module 3: Risk, Accountability, and Impact Management - Assessment15 minutes

In this module, we will examine cybersecurity, privacy, and compliance challenges related to AI system deployment. We will explore governance controls that ensure safe implementation, monitoring, and incident management. Learners will also understand how organizations address bias, misuse, and security risks in real-world AI deployments.

What's included

16 videos1 assignment

16 videosTotal 60 minutes

Understanding Deployment Readiness and Use Case Alignment4 minutes
Matching AI Model Types to Deployment Needs4 minutes
Deployment Options: Cloud, Edge, and Fine-Tuning Considerations4 minutes
Pre-Deployment Impact Assessment3 minutes
Legal and Contractual Review (Vendor/License)4 minutes
Governance for Proprietary Models vs. Third-Party Components4 minutes
Applying Policies in Deployment (Operations, Training)4 minutes
Security and Performance Monitoring After Deployment4 minutes
Incident Management in Deployment4 minutes
Preventing Misuse and Managing Secondary AI Harms3 minutes
Transparency and External Communication4 minutes
Contingency Planning and Deactivation Controls4 minutes
Bias and Fairness in Deployment4 minutes
Compliance in Deployment (Post-Market Monitoring)4 minutes
AI Safety and Societal Impact3 minutes
Periodic Review and Security Testing4 minutes

1 assignmentTotal 15 minutes

Domain 4 - Module 4: Cybersecurity and Data Privacy Concerns - Assessment15 minutes

In this module, we will explore practical case studies that demonstrate how AI governance operates in real-world environments. We will examine examples from financial services, healthcare, autonomous transportation, and smart city infrastructure. These examples will help learners connect governance frameworks with practical implementation challenges and solutions.

What's included

6 videos1 assignment

6 videosTotal 15 minutes

What to Expect in This Section1 minute
AI Governance in Financial Services3 minutes
AI Governance in Healthcare and Medical Technologies3 minutes
AI Governance in Autonomous Transportation3 minutes
AI Governance in Smart Cities and Urban Infrastructure3 minutes
Section Conclusion – Key Takeaways1 minute

1 assignmentTotal 15 minutes

Overview: AI Governance Case Studies - Assessment15 minutes

In this module, we will summarize the key lessons and insights from the entire AI governance course. We will revisit the most important frameworks, principles, and governance practices discussed in previous modules. Learners will leave with a clear understanding of how to apply AI governance knowledge in professional and organizational contexts.

What's included

1 video2 assignments

Instructor

Packt - Course Instructors

Packt

1,674 Courses477,715 learners

Offered by

Packt

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Frequently asked questions

AI Governance refers to the frameworks, policies, processes, and oversight mechanisms used to ensure artificial intelligence systems are developed and deployed responsibly, ethically, and in compliance with legal and regulatory standards. As AI becomes more integrated into business, healthcare, finance, and public systems, governance helps manage risks such as bias, privacy violations, security threats, and societal harm. Effective AI governance ensures transparency, accountability, and trust in AI systems while enabling organizations to innovate responsibly.

This course provides a comprehensive preparation path for the AIGP certification and focuses on the principles, frameworks, and practical implementation of AI governance. It covers the foundations of AI governance, responsible AI principles, legal and regulatory frameworks such as the EU AI Act and international standards, risk management strategies, and cybersecurity and privacy concerns in AI systems. Through structured modules and real-world case studies across industries like finance, healthcare, and smart cities, learners gain a full understanding of how to govern AI systems throughout their lifecycle.

After completing this course, you will understand how to design and implement AI governance frameworks within organizations. You will be able to assess AI risks, apply legal and ethical standards, manage AI accountability and transparency, and oversee AI systems from development through deployment and monitoring. The course also prepares you with the knowledge required for the AIGP certification exam and equips you to contribute to responsible AI strategies, compliance initiatives, and governance programs in professional environments.

This course is designed to be accessible to learners with a general understanding of technology, compliance, or risk management, but it does not require advanced technical expertise in artificial intelligence. Basic familiarity with concepts related to data, privacy, governance, or technology policy can be helpful, but the course explains AI concepts, governance principles, and regulatory frameworks from the ground up to ensure learners from diverse professional backgrounds can follow along.

This course is ideal for professionals involved in AI governance, compliance, privacy, risk management, cybersecurity, and technology leadership. It is particularly useful for policy professionals, data protection officers, legal experts, AI project managers, and consultants who need to understand how to govern AI responsibly. It is also suitable for professionals preparing for the AIGP certification or anyone interested in building expertise in ethical and regulatory aspects of artificial intelligence.

The course is designed to be completed in approximately 5 hours. It is structured into multiple domains and modules that cover AI governance foundations, legal frameworks, risk management, cybersecurity considerations, and real-world case studies. Learners can progress through the lessons at their own pace, making it suitable for busy professionals who want a focused yet comprehensive overview of AI governance.