IAPP AIGP Complete Training – AI Governance Mastery

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

8 weeks, 2 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English, Arabic, Korean, Portuguese, Japanese, Italian, French, Spanish

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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 practical AI governance knowledge while preparing for the IAPP AIGP certification. Learn to evaluate AI risks, responsible AI principles, governance controls, and regulatory obligations. Begin with AI foundations, machine learning, governance frameworks, harms, privacy, ethics, and trustworthy AI. Then explore organizational governance, accountability, risk assessment, and the NIST AI Risk Management Framework. Next, follow AI systems across development and deployment, covering impact assessments, data governance, testing, monitoring, model cards, third-party risk, auditing, RAG, and agentic AI. Examine the EU AI Act alongside global laws and ISO standards. This intermediate course is designed for privacy, compliance, risk, legal, technology, and AI governance professionals. Basic familiarity with AI or technology concepts is helpful. By the end of the course, you will be able to evaluate AI risks, design governance controls, interpret major regulatory requirements, manage AI lifecycle risks, and apply responsible AI practices while preparing for the AIGP exam.

Syllabus

Foundations of Artificial Intelligence and AI Governance
- In this module, we will establish the foundations of artificial intelligence and AI governance, covering AI types, machine learning, generative AI, transformers, and common organizational use cases. We will examine the AI technology stack, compute infrastructure, and model relationships while exploring how AI systems are developed and trained. We will also introduce governance principles, frameworks, observability, and monitoring practices essential for responsible AI.
AI Impacts on People and Responsible AI Principles
- In this module, we will examine how AI can affect individuals, organizations, and society through privacy, bias, discrimination, security, and operational risks. We will explore harm taxonomies, OECD guidelines, ethical considerations, and the characteristics of trustworthy AI. We will also learn how governance controls, organizational culture, and responsible AI practices can help mitigate these risks.
Responsible AI Governance and Risk Management
- In this module, we will explore how organizations can establish effective AI governance structures, policies, roles, and accountability mechanisms. We will examine stakeholder engagement, leadership support, training, and alignment between AI initiatives and enterprise risk strategies. We will also apply risk assessment techniques and explore the NIST AI Risk Management Framework and ARIA Program.
Governing AI Development: Lifecycle, Data, and Model Controls
- In this module, we will examine governance across the AI development lifecycle, from defining business problems and scope to testing, validation, and monitoring. We will explore stakeholder engagement, impact assessments, data governance, lineage, preparation, privacy-enhancing technologies, and feature engineering. We will also examine documentation practices such as model cards and conformity documentation that strengthen transparency and accountability.
Governing AI Deployment and Operational Risk
- In this module, we will explore the governance controls required to deploy and operate AI systems responsibly, including privacy, security, vendor, and third-party considerations. We will examine monitoring, model drift, periodic assessments, incident response, auditing, and transparency obligations. We will also compare deployment approaches such as cloud, on-premises, edge, fine-tuning, RAG, and agentic AI architectures.
The EU AI Act: Risk Classification, Obligations, and Compliance
- In this module, we will examine the EU AI Act, including its scope, exemptions, terminology, AI literacy requirements, and risk-based classification system. We will explore obligations for providers, deployers, importers, and distributors, along with requirements for high-risk and general-purpose AI. We will also address transparency, documentation, human oversight, quality management, enforcement, and penalties.
Other Laws and Standards Related to AI
- In this module, we will explore the wider legal and standards landscape influencing AI governance, including U.S. federal, state, and local requirements and international regulatory developments. We will examine privacy principles, automated decision-making rules, bias audit requirements, and practical obligations for AI providers and deployers. We will also explore ISO/IEC 22989, ISO/IEC 42001, and ISO/IEC 42005 and their roles in strengthening AI governance.
Final Course Summary and Key Takeaways
- In this module, we will consolidate the key concepts, frameworks, regulations, and governance practices covered throughout the course. We will reinforce essential knowledge spanning responsible AI, risk management, lifecycle controls, the EU AI Act, and other relevant laws and standards. We will connect these concepts to practical AI governance responsibilities and focused preparation for the IAPP AIGP certification exam.