Certified: The IAPP AIGP Audio Course

This episode clarifies role categories that matter because legal duties and operational responsibilities often depend on where an organization sits in the AI value chain. You will learn how developers build or significantly shape systems, providers place systems into the market or make them available under their name, deployers use those systems in their own operations, and users interact with out...

This episode teaches an important exam concept: governance should be proportionate to context. You will examine why a small company testing a narrow internal AI tool does not need the same structure as a global enterprise deploying high-impact systems across regulated markets, even though both still need accountability, controls, and oversight. The episode breaks down how company size affects staf...

This episode shows why AI training must be tailored to role and responsibility rather than delivered as a generic awareness session to everyone. You will learn how frontline users, executives, developers, procurement teams, privacy staff, security professionals, and governance committees need different levels of depth, different examples, and different action triggers. The exam may frame this as a...

This episode explains how effective AI governance depends on collaboration between groups that often speak different professional languages and pursue different goals. You will explore how legal, compliance, privacy, security, data science, engineering, procurement, HR, and business units must coordinate without creating endless approval loops that slow useful work. The exam may test this through...

This episode focuses on one of the most common governance failures in both exam scenarios and real organizations: unclear ownership. You will learn how AI governance depends on defined roles for business leaders, legal teams, privacy professionals, security teams, data stewards, model developers, product owners, procurement staff, audit functions, and senior decision-makers. The key point is that...

This episode turns high-level responsible AI principles into practical decision lenses you can use on the exam. You will examine fairness as more than equal treatment, safety as more than cybersecurity, privacy as more than notice language, transparency as more than publishing a policy, and accountability as more than naming an owner. The goal is to understand how these principles interact, becaus...

This episode explains why AI governance exists by focusing on the gap between technical performance and real-world harm. You will learn the difference between risks to the organization and harms to people, groups, markets, or institutions, and why both matter on the exam and in practice. The discussion covers familiar problems such as bias, privacy intrusion, security weakness, opacity, overrelian...

This episode builds the vocabulary needed to understand later governance topics by separating broad AI concepts from narrower technical categories that often appear on the exam. You will review what artificial intelligence generally means in practice, how machine learning differs from rules-based automation, and why generative systems, predictive systems, recommendation systems, classification mod...

This episode introduces the structure of the AIGP exam so you can study with intention instead of collecting disconnected facts. You will learn how exam domains signal what the certifying body expects you to know, how objective language can hint at the depth of understanding being tested, and why terms such as identify, evaluate, compare, and apply often point to different question styles. The epi...