AIGP Cert Masterclass - Prepare for the Exam in 2026

This course contains the use of artificial intelligence. However, every lecture recording involves me reading the scripts, and I am fully involved in scripting and production. Be careful buying courses with instructors that don't appear in person.

AI courses are becoming quite common on learning platforms. This course is a complete, structured study program for the IAPP Artificial Intelligence Governance Professional (AIGP) exam. Built domain by domain against the official exam blueprint, it covers every topic area you need to understand before sitting for the exam.

Each lesson is a narrated video that explains how concepts connect to each other and to real-world practice — not just what the definition is, but how a practitioner applies it. D1 — Understanding the foundations of AI governance (21% of the exam) — covers define ai and identify types of ai systems including machine learning, deep learning, generative ai, and large language models, identify types of risk and harm associated with ai systems including performance risk, bias risk, privacy risk, security risk, and societal harm, explain unique characteristics of ai that require governance including opacity, autonomy, scalability, and emergent behavior, describe common principles of responsible ai including fairness, transparency, accountability, human oversight, non-maleficence, privacy, and autonomy, identify roles and responsibilities for ai governance including ai owner, model owner, ai risk officer, ethics board, and board-level oversight, describe how cross-functional collaboration among legal, privacy, security, data management, hr, and business teams supports effective ai governance, explain how training and awareness programs communicate ai governance expectations to employees, contractors, and affected stakeholders, differentiate governance approaches appropriate to different company sizes and ai maturity levels, distinguish obligations and accountability of ai developers, providers, deployers, and users across the ai value chain, identify the policies and procedures needed to establish oversight and accountability across each ai lifecycle stage from design through decommissioning, describe how to evaluate and update existing organizational policies covering privacy, security, data governance, and intellectual property to address ai-specific requirements, explain third-party risk policies covering ai procurement, supply chain due diligence, hr practices, and acceptable use requirements for ai tools. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.

D2 — Understanding how laws, standards and frameworks apply to AI (25% of the exam) — covers explain how transparency, choice, lawful basis, and purpose limitation requirements under gdpr and other privacy laws apply to ai systems, describe how data minimization and privacy by design principles constrain ai training data selection and system architecture, identify controller obligations relevant to ai including data protection impact assessments, processor agreements, cross-border transfer mechanisms, data subject access requests, automated decision-making restrictions under gdpr article 22, and data breach notification, explain how special categories of personal data including health, biometric, and genetic data attract heightened obligations when used in ai systems, describe how intellectual property laws including copyright, trade secret, and patent apply to ai-generated content, training data, and model outputs, explain how nondiscrimination laws apply to ai-driven decisions in employment, credit, housing, and other regulated domains including disparate impact liability, identify how consumer protection laws and regulations apply to ai claims, disclosures, and automated practices, describe how product liability frameworks apply to ai systems including provider and deployer exposure for harm caused by ai outputs, classify ai systems under risk-tiered regulatory frameworks including the prohibited, high-risk, limited-risk, and minimal-risk categories of the eu ai act, describe requirements for high-risk ai systems including risk management systems, data governance, technical documentation, and conformity assessment procedures, explain human oversight requirements, transparency obligations, and quality management system requirements for high-risk ai systems, identify obligations applicable to general-purpose ai (gpai) models including transparency requirements, copyright compliance, technical documentation, and systemic risk designation thresholds, describe enforcement mechanisms, penalties, and market surveillance powers under ai-specific laws, identify how organizational context including provider, deployer, importer, and distributor roles affects compliance obligations under ai-specific laws, describe the oecd ai principles and how they inform voluntary and regulatory ai governance approaches across jurisdictions, explain the nist ai rmf core functions (govern, map, measure, manage), categories, subcategories, and the nist ai rmf playbook as an implementation tool, identify the purpose and scope of core iso standards relevant to ai governance including iso 22989 (ai concepts and terminology), iso 42001 (ai management systems), and iso 42005 (ai system impact assessment). You will understand how each of these areas is tested on the exam and how they connect to real-world practice. D3 — Understanding how to govern AI development (27% of the exam) — covers describe how to define and document an ai use case including intended purpose, success criteria, stakeholder requirements, and constraints prior to design, explain how to conduct an impact assessment during the design phase to identify potential harms to individuals, groups, and society, identify how to apply organizational policies and best practices to ai system design including privacy by design, security by design, and responsible ai principles, describe how to identify and manage internal and external risks during the design and build phases including bias, security vulnerabilities, and dependency risks, explain requirements for documenting the design and build process including architecture decisions, data sources, training approaches, and known limitations, identify data governance requirements applicable to ai training and testing data including lawful basis, data minimization, purpose limitation, and quality standards, describe data lineage and provenance requirements for training and testing datasets including source documentation, consent records, and licensing terms, explain how to plan and execute training and testing activities including dataset splitting, evaluation methodology, and acceptance criteria definition, identify common training and testing issues and risks including data imbalance, leakage, overfitting, and benchmark gaming, describe documentation requirements for training and testing activities including datasheets for datasets, training run records, and test results, identify production readiness requirements including conformity assessment, model cards, deployment authorization gates, and rollback procedures, explain how to design and operate a continuous monitoring program for deployed ai systems covering performance, bias, drift, and security, describe periodic assessment activities including audits, red-teaming, threat modeling, and adversarial testing, identify incident management requirements for ai systems including classification criteria, containment options, regulatory notification obligations, and evidence preservation, explain how to collaborate with technical teams on incident root cause analysis and remediation to prevent recurrence, describe public disclosure requirements including technical documentation for users, instructions for use, and post-market monitoring plans.

You will understand how each of these areas is tested on the exam and how they connect to real-world practice. D4 — Understanding how to govern AI deployment and use (27% of the exam) — covers describe the ai use case context factors relevant to a deployment decision including affected populations, regulatory environment, data sensitivity, and organizational risk appetite, distinguish ai model types relevant to deployment decisions including classical machine learning versus generative ai, proprietary versus open-source models, and foundation models versus task-specific models, identify ai deployment options and their governance implications including cloud versus on-premises versus edge deployment, and fine-tuning versus retrieval-augmented generation versus agentic architectures, explain how to conduct an impact assessment prior to deployment including evaluating potential harms, affected populations, and mitigation options, describe how to review vendor documentation and licensing terms including model cards, technical specifications, data use restrictions, and liability allocations, identify unique risks faced by deployers of proprietary ai models including dependency on provider transparency, limited auditability, and inherited compliance obligations, explain how to apply organizational deployment policies including acceptable use requirements, access controls, and human oversight configurations, describe how to design and operate a continuous monitoring program for deployed ai systems covering performance degradation, fairness drift, and misuse detection, identify periodic assessment activities for deployed systems including compliance audits, penetration testing, and bias re-evaluation, explain incident documentation requirements for deployment-phase incidents including logs, root-cause records, and regulatory notification artifacts, describe methods for forecasting secondary and unintended uses of a deployed ai system and governance controls to prevent misuse, explain how to develop external communications plans covering ai disclosures to users, regulators, and the public, identify deactivation and localization controls including procedures to suspend ai decision-making, restrict geographic scope, and manage data residency requirements. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.

Every domain includes practice questions designed to mirror the style and difficulty of AIGP exam scenarios, covering not just recall but application and analysis. The course closes with full-length practice exams with detailed answer explanations, so you can measure your readiness and focus your remaining study time where it matters most. Major topics covered: define ai and identify types of ai systems including machine learning, deep learning, generative ai, and large language models, identify types of risk and harm associated with ai systems including performance risk, bias risk, privacy risk, security risk, and societal harm, explain unique characteristics of ai that require governance including opacity, autonomy, scalability, and emergent behavior, describe common principles of responsible ai including fairness, transparency, accountability, human oversight, non-maleficence, privacy, and autonomy, identify roles and responsibilities for ai governance including ai owner, model owner, ai risk officer, ethics board, and board-level oversight, describe how cross-functional collaboration among legal, privacy, security, data management, hr, and business teams supports effective ai governance, explain how training and awareness programs communicate ai governance expectations to employees, contractors, and affected stakeholders, differentiate governance approaches appropriate to different company sizes and ai maturity levels, distinguish obligations and accountability of ai developers, providers, deployers, and users across the ai value chain, identify the policies and procedures needed to establish oversight and accountability across each ai lifecycle stage from design through decommissioning, describe how to evaluate and update existing organizational policies covering privacy, security, data governance, and intellectual property to address ai-specific requirements, explain third-party risk policies covering ai procurement, supply chain due diligence, hr practices, and acceptable use requirements for ai tools, explain how transparency, choice, lawful basis, and purpose limitation requirements under gdpr and other privacy laws apply to ai systems, describe how data minimization and privacy by design principles constrain ai training data selection and system architecture, identify controller obligations relevant to ai including data protection impact assessments, processor agreements, cross-border transfer mechanisms, data subject access requests, automated decision-making restrictions under gdpr article 22, and data breach notification, explain how special categories of personal data including health, biometric, and genetic data attract heightened obligations when used in ai systems, describe how intellectual property laws including copyright, trade secret, and patent apply to ai-generated content, training data, and model outputs, explain how nondiscrimination laws apply to ai-driven decisions in employment, credit, housing, and other regulated domains including disparate impact liability, identify how consumer protection laws and regulations apply to ai claims, disclosures, and automated practices, describe how product liability frameworks apply to ai systems including provider and deployer exposure for harm caused by ai outputs, classify ai systems under risk-tiered regulatory frameworks including the prohibited, high-risk, limited-risk, and minimal-risk categories of the eu ai act, describe requirements for high-risk ai systems including risk management systems, data governance, technical documentation, and conformity assessment procedures, explain human oversight requirements, transparency obligations, and quality management system requirements for high-risk ai systems, identify obligations applicable to general-purpose ai (gpai) models including transparency requirements, copyright compliance, technical documentation, and systemic risk designation thresholds, describe enforcement mechanisms, penalties, and market surveillance powers under ai-specific laws, identify how organizational context including provider, deployer, importer, and distributor roles affects compliance obligations under ai-specific laws, describe the oecd ai principles and how they inform voluntary and regulatory ai governance approaches across jurisdictions, explain the nist ai rmf core functions (govern, map, measure, manage), categories, subcategories, and the nist ai rmf playbook as an implementation tool, identify the purpose and scope of core iso standards relevant to ai governance including iso 22989 (ai concepts and terminology), iso 42001 (ai management systems), and iso 42005 (ai system impact assessment), describe how to define and document an ai use case including intended purpose, success criteria, stakeholder requirements, and constraints prior to design, AIGP exam prep.