AIGP Certification Prep Course: IAPP Official Guide 2026

Unlock the secrets to building trustworthy and compliant AI systems. This lecture demystifies data governance, teaching you how to classify data, establish clear ownership, and ensure data quality. Through real-world examples from healthcare and retail, you'll learn to create a robust data governance framework that is essential for managing risk and building a foundation for responsible AI. Don't let bad data undermine your AI; master the principles of data governance and set your AI initiatives up for success.

Navigate the complex legal landscape of AI and intellectual property. This lecture breaks down the critical IP issues every AI professional must understand, from copyright challenges with training data to the ownership of AI models and their generated content. Learn how to protect your company’s most valuable assets using patents, trade secrets, and strategic policies. This essential guide will equip you to make informed decisions and avoid costly legal battles in the fast-evolving world of AI.

Leveraging third-party AI can accelerate innovation, but it comes with significant risks. This lecture provides a comprehensive framework for managing those risks effectively. You'll learn a two-phase due diligence process, master the art of the vendor risk assessment questionnaire, and discover the importance of independent technical validation. Most importantly, you'll understand the key contractual protections needed to safeguard your organization, ensuring you can confidently partner with AI vendors while minimizing liability.

Once an AI tool is approved, how do you ensure employees use it responsibly? This lecture guides you through creating a powerful Acceptable Use Policy (AUP) for AI. Learn to define approved tools and use cases, establish clear “red lines” for data handling to prevent leaks of sensitive information, and clarify IP ownership of AI-generated content. Empower your team to innovate safely and effectively by implementing a clear and comprehensive AUP that protects both your employees and your organization.

Are you governing the 'brain' or the entire 'body'? This lecture clarifies the critical distinction between an AI model and a complete AI system—a concept many professionals miss. Understanding this difference is fundamental to effective governance. You'll learn why model governance focuses on technical integrity and bias, while system governance addresses real-world impact and user safety. This crucial lesson will reshape how you approach AI risk management, ensuring your governance framework is truly comprehensive.

Transparency in AI isn't just a best practice; it's the law. This lecture dives into the transparency requirements of major data privacy laws like GDPR and CCPA. You'll learn what you must disclose to individuals when using AI to make decisions about them, from the logic involved to the potential consequences. Master the art of crafting clear and compliant privacy notices for your AI systems, building trust with your users and staying on the right side of regulators.

Processing personal data without a valid legal reason is a costly mistake. This lecture provides a clear guide to the concept of "lawful basis" under GDPR. You'll explore the six lawful bases—including consent, contract, and legitimate interests—and learn how to choose the right one for your AI system. Avoid massive fines and build a compliant data processing strategy by mastering this foundational principle of data privacy law.

This lecture unpacks one of the most important and challenging regulations for AI: GDPR's Article 22. Discover when you are prohibited from making fully automated decisions that have a legal or significant impact on individuals. We'll explore the critical exceptions to this rule and the absolute right to human review. Understanding Article 22 is non-negotiable for anyone deploying AI in Europe, as non-compliance can lead to severe penalties.

When your AI makes a life-altering decision, can you explain why? This lecture tackles the crucial concepts of explainability and contestability. You'll learn techniques to interpret 'black box' models and provide meaningful explanations to individuals. Furthermore, you'll discover how to build robust processes that allow people to challenge and contest automated decisions. Move beyond theory and learn how to implement these essential pillars of fair and accountable AI.

South Korea is a global AI powerhouse, and its AI Basic Law is a comprehensive framework you need to understand. This lecture provides a concise overview of this pioneering legislation, covering its core principles of responsible AI, transparency, fairness, and human oversight. Get ahead of the curve and learn from South Korea’s balanced approach to fostering AI innovation while protecting society.

Ready to move from theory to practice? This lecture provides a step-by-step compliance roadmap for South Korea's AI Basic Law. Learn how to establish governance structures, implement robust data protection measures, and conduct bias testing to meet the law's stringent fairness requirements. This practical guide is essential for any organization operating in or targeting the South Korean market.

The U.S. has no federal AI law, creating a complex patchwork of state-level regulations. This lecture focuses on two of the most important: Colorado's AI Transparency Act and California's influential CCPA. Understand the key requirements, similarities, and differences between these laws to navigate the fragmented American AI governance landscape and ensure compliance.

AI regulation in the U.S. gets even more complex with laws targeting specific industries. This lecture dives into sector-specific AI legislation for high-stakes areas like hiring, healthcare, and financial services. Learn about the unique compliance challenges and regulatory expectations in these critical sectors to avoid costly legal and reputational damage.

Go beyond legal compliance and learn to build a world-class AI governance program with ISO/IEC 42005. This lecture introduces you to the internationally recognized standard for AI management system governance. Understand its core principles and key components, including risk management, data governance, and model governance, to demonstrate your commitment to responsible AI.

This lecture takes you from understanding to implementation. Learn how to put the ISO/IEC 42005 standard into practice with a focus on concrete controls, documentation, and metrics. We'll cover everything from establishing governance committees to implementing incident reporting and regular audits, providing you with a practical toolkit for building a robust and certifiable AI management system.

This lecture revisits the crucial distinction between AI models and systems, but this time through the lens of development. You'll learn why governing the development of a model is a highly technical process focused on data and algorithms, while governing the development of a system is a holistic endeavor that includes user experience, risk assessment, and real-world impact. Mastering this distinction is key to building truly responsible AI products.

"Garbage in, garbage out" is the golden rule of AI. This lecture provides a deep dive into data governance throughout the AI development lifecycle. From ethical data sourcing and preparation to ensuring data quality and fairness, you'll learn the practical steps needed to build a solid data foundation for your AI systems. Don't let poor data practices derail your AI projects; master the art of data governance.

Welcome to the future of AI. This lecture introduces you to the exciting world of agentic AI—systems that can act autonomously to achieve goals. You'll learn about the different types of agentic architectures, their incredible potential, and the profound new governance challenges they present. This is essential knowledge for anyone looking to stay at the forefront of AI.

With great autonomy comes great responsibility. This lecture tackles the unique and complex governance challenges posed by agentic AI. How do you ensure the safety and predictability of a system that can act on its own? We'll explore the need for new oversight mechanisms, the limits of traditional risk management, and the critical importance of human-in-the-loop design for these powerful systems.

From personalized assistants to automated scientific discovery, agentic AI is already here. This lecture examines real-world applications of agentic systems and the specific governance frameworks being developed to manage them. Learn from case studies and best practices to understand how to harness the power of agentic AI while mitigating its risks.

Your work isn't done when an AI model is deployed. This lecture covers the critical importance of continuous monitoring and performance management. You'll learn how to detect and diagnose common post-deployment issues like performance degradation, data drift, and concept drift. Master the tools and techniques needed to ensure your AI models remain accurate, fair, and reliable over time.

This lecture takes your third-party risk management skills to the next level. Go beyond initial due diligence and learn how to implement a program of continuous monitoring for your AI vendors. We'll cover advanced topics like ongoing performance validation, managing contractual obligations, and developing robust exit strategies. This is essential for any organization that relies on external AI services.

Using third-party AI APIs and SaaS tools can be a huge accelerator, but it also introduces new risks. This lecture provides a clear framework for governing the use of these services. You'll learn about critical data privacy and security considerations, the importance of data minimization, and how to conduct effective vendor due diligence. Ensure you can leverage the power of the cloud without compromising on governance.