AI Governance and Privacy Professional Certification (AIGP)

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Overview

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This course features Coursera Coach! 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. Build the knowledge needed to govern AI systems responsibly, manage privacy risks, and prepare for AIGP certification. You will learn how AI systems work, why they create new accountability challenges, and how governance frameworks support trustworthy AI. The course begins with the AIGP exam structure, core AI concepts, machine learning foundations, OECD classification, socio-technical systems, data governance, intellectual property, and third-party AI risk. You will then explore AI harms, responsible AI principles, privacy law requirements, automated decision-making, organizational governance models, stakeholder engagement, risk assessment, and development lifecycle controls. Later modules focus on deployment governance, monitoring, model drift, incident response, agentic AI, vendor oversight, the EU AI Act, global AI laws, GDPR, DPIAs, ISO standards, non-discrimination, consumer protection, and product liability. This intermediate course is ideal for privacy, compliance, legal, risk, security, product, and technology professionals. Basic familiarity with AI, privacy, or governance concepts is helpful. By the end of the course, you will be able to evaluate AI risks, apply responsible AI principles, support governance programs, interpret major AI regulations, and prepare for AIGP-style exam questions.

Syllabus

Introduction

In this module, we will introduce the AI Governance and Privacy Professional (AIGP) certification and the path toward becoming an AI governance leader. We will explore the structure of the exam, the competencies outlined in the AIGP Body of Knowledge, and the skills needed to succeed in the field. We will also examine Bloom’s Taxonomy and how it shapes the design and complexity of AIGP certification questions.

Understanding Foundation of AI

In this module, we will build a strong foundational understanding of artificial intelligence, machine learning, and modern AI ecosystems. We will explore AI system classifications, socio-technical dynamics, governance challenges, and the growing influence of AI across industries and society. We will also examine practical governance topics including data governance, third-party risk management, intellectual property, and AI system accountability.

AI Impacts on People and Responsible AI Principles

In this module, we will examine the societal, organizational, and individual impacts of artificial intelligence systems. We will explore responsible AI principles, ethical risk mitigation strategies, privacy requirements, and frameworks for trustworthy AI governance. We will also review global AI regulations, transparency obligations, and international standards that shape responsible AI development and deployment.

Responsible AI Governance and Risk Management

In this module, we will explore how organizations establish and operationalize responsible AI governance programs. We will examine governance structures, stakeholder collaboration, leadership engagement, and enterprise risk management strategies. We will also review practical frameworks for AI assessments, oversight mechanisms, and governance processes that support trustworthy AI systems.

Governing AI Development

In this module, we will examine governance practices applied throughout the AI development lifecycle. We will explore business problem definition, impact assessments, risk management, and technical design considerations for AI systems. We will also cover data governance, documentation practices, model validation, and feature engineering techniques essential for responsible AI development.

Governing AI Deployment

In this module, we will explore the governance challenges and operational considerations involved in deploying AI systems into production environments. We will examine third-party risk management, model monitoring, incident response, and governance requirements for AI APIs and SaaS tools. We will also discuss autonomous AI systems, agentic architectures, and strategies for maintaining accountability and transparency in deployed AI solutions.

The EU AI Act

In this module, we will explore the EU AI Act and its role as one of the most influential AI regulations in the world. We will examine risk classifications, stakeholder responsibilities, prohibited AI systems, and compliance obligations for high-risk AI applications. We will also review governance structures, enforcement mechanisms, and the regulatory treatment of general-purpose AI models under the Act.

Other Laws and Standards Related to AI

In this module, we will explore global AI laws, regulations, and standards that shape responsible AI governance practices. We will examine privacy laws, GDPR obligations, international standards, and human rights-based approaches to AI oversight. We will also discuss intellectual property, discrimination, consumer protection, and compliance considerations for organizations deploying AI systems worldwide.