- Overview of AI governance and its importance in ethical AI development and deployment.
- Role of the AIGP certification in establishing expertise in responsible AI practices.
- Key principles of AI governance, including accountability, transparency, and fairness.
- Aligning AI governance with organizational objectives and regulatory requirements.
IAPP AIGP exam preparation
IAPP AIGP Certified AI Governance Professional Certification Training Course
A comprehensive understanding of AI governance and responsible AI practices as outlined by the IAPP. Align AI governance with organizational objectives, manage AI-related risks, and implement responsible AI strategies.
Course focus: AI governance frameworks, risk management, ethical considerations, and integration of privacy and accountability into AI systems.
Course Overview
Responsible AI governance skills for ethical development, risk, and compliance.
The IAPP Certified AI Governance Professional (AIGP) Training Course with Accumentum is designed to provide a comprehensive understanding of AI governance and responsible AI practices as outlined by the IAPP. The training begins with an introduction to AI governance, emphasizing its critical role in ensuring ethical AI development, deployment, and regulatory compliance. Key modules cover AI governance frameworks, risk management, ethical considerations, and integration of privacy and accountability into AI systems. Participants will learn to align AI governance with organizational objectives, manage AI-related risks, and implement responsible AI strategies.
The course combines interactive lectures, real-world AI governance case studies, and group activities to promote practical application of concepts. It also includes exam preparation strategies with mock tests to familiarize learners with the AIGP certification exam format. This thorough approach ensures participants are well-equipped to address the complexities of AI governance in their professional settings.
Course Objectives
What you will be able to do in AI governance roles.
01
Develop AI governance frameworks
Equip participants with the skills to develop and implement AI governance frameworks that ensure ethical and compliant AI systems.
02
Manage AI-related risks
Develop expertise in managing AI-related risks, including privacy, bias, and regulatory challenges.
03
Align governance with goals
Provide practical strategies for aligning AI governance with organizational goals and fostering responsible AI practices.
04
Prepare for AIGP certification
Prepare participants for the AIGP certification exam through targeted training, mock tests, and real-world AI governance scenarios.
Who Should Attend
- AI Governance Professionals: Individuals responsible for overseeing ethical AI development and deployment within organizations.
- Data Privacy and Compliance Officers: Professionals seeking to integrate AI governance with privacy and regulatory requirements.
- Technology and Risk Managers: Those focused on managing AI-related risks, including bias, ethics, and compliance challenges.
- Aspiring AI Governance Experts: Individuals aiming to earn the AIGP certification to advance their careers in responsible AI management.
Prerequisites
- Basic Knowledge of AI and Privacy: Familiarity with fundamental AI concepts and privacy principles is recommended but not mandatory.
- Professional Experience: At least one to two years of experience in AI, data protection, compliance, or governance roles is preferred.
- Interest in AI Governance: A strong interest in learning about ethical AI practices and regulatory frameworks for AI systems.
- No Formal Certification Required: No prior certifications are necessary, though a background in AI, IT, or privacy is advantageous.
Pathway Map
Ten modules from AI governance foundations to exam-ready case studies.
Each module builds the responsible AI knowledge assessed on the AIGP exam, including ethics, risk, privacy by design, regulation, and accountability.
1
Introduction to AI Governance
2
AI Technologies and Their Privacy Implications
3
Ethical AI Principles and Practices
4
AI Governance Frameworks
5
Risk Management in AI Systems
6
Privacy by Design in AI Development
7
Regulatory Compliance for AI Systems
8
Stakeholder Engagement and Transparency
9
Incident Response and AI Accountability
10
Exam Preparation and AI Governance Case Studies
Course Content
- Understanding core AI technologies, including machine learning, neural networks, and generative AI.
- Identifying privacy risks associated with AI data processing and decision-making.
- Exploring the intersection of AI, personal data, and global privacy regulations (e.g., GDPR, CCPA).
- Case studies on privacy challenges in AI-driven applications.
- Core ethical principles for AI, including fairness, accountability, and inclusivity.
- Strategies for mitigating bias and ensuring equitable AI outcomes.
- Balancing innovation with ethical considerations in AI development.
- Frameworks for embedding ethical AI practices into organizational processes.
- Developing comprehensive AI governance frameworks for organizational compliance.
- Establishing policies, roles, and responsibilities for AI oversight.
- Integrating AI governance with existing privacy and risk management programs.
- Tools and methodologies for effective AI governance implementation.
- Conducting risk assessments for AI systems, focusing on privacy, bias, and security risks.
- Identifying and prioritizing AI-specific risks in development and deployment phases.
- Developing mitigation strategies to address AI-related vulnerabilities.
- Continuous monitoring and auditing of AI systems for risk management.
- Principles of privacy by design and default in AI system architecture.
- Incorporating privacy-enhancing technologies (PETs) into AI models and processes.
- Managing data minimization, anonymization, and consent in AI applications.
- Practical approaches to embedding privacy into the AI development lifecycle.
- Overview of global AI regulations and their impact on development and deployment.
- Navigating compliance with privacy laws (e.g., GDPR, CCPA) in AI contexts.
- Addressing emerging AI-specific regulations and guidelines worldwide.
- Strategies for ensuring compliance in cross-border AI deployments.
- Engaging stakeholders, including developers, regulators, and end-users, in AI governance.
- Communicating AI policies, risks, and ethical considerations effectively.
- Building transparency in AI decision-making processes and outcomes.
- Fostering trust through clear documentation and stakeholder collaboration.
- Developing incident response plans for AI-related privacy and ethical breaches.
- Legal and regulatory requirements for reporting AI incidents and failures.
- Investigating and addressing AI system errors, biases, or misuse.
- Real-world case studies on managing AI incidents and ensuring accountability.
- Overview of the AIGP certification exam structure and question formats.
- Effective study strategies and time management for exam success.
- Analysis of real-world AI governance case studies to apply learned concepts.
- Practice with mock exams to build confidence and familiarity with the test format.
Course Features
Interactive AIGP exam preparation with Accumentum.
Interactive Learning
Engage with expert instructors and peers through training sessions, discussions, and practical exercises.
Comprehensive Study Materials
Access extensive resources, including e-books, video lectures, and practice exams.
Real-World Applications
Work on real-life case studies and scenarios to apply effective AI Governance concepts.
Certification Preparation
Receive guidance and tips to successfully pass the IAPP AIGP certification exam.
Certification Exam
Prepare for the official IAPP AIGP exam.
AIGP Exam Readiness
Accumentum AIGP training prepares learners for the IAPP Certified AI Governance Professional exam.
Upon completing the IAPP Certified AI Governance Professional (AIGP) Training Course with Accumentum, you will be fully prepared to take the AIGP certification exam. This certification validates your expertise in AI governance frameworks, ethical AI practices, and responsible AI management, demonstrating your ability to align AI governance with organizational objectives, manage AI-related risks, and apply best practices for compliance and ethical AI deployment. Earning the AIGP certification will significantly advance your career, positioning you for leadership roles in overseeing AI governance initiatives and driving responsible AI strategies.
Exam Blueprint
The full AIGP body of knowledge.
The IAPP AIGP Body of Knowledge and Exam Blueprint (version 2.1, effective 2 February 2026) has 4 domains, 13 competencies and 58 performance indicators. The IAPP says no question will be on the exam that isn't covered by the body of knowledge, so we've copied every line below in the IAPP's own wording. The numbers show the minimum and maximum questions the blueprint sets for each area.
Domain I: Understanding the foundations of AI governance (16 to 20 questions)
Domain I, Understanding the foundations of AI governance focuses on what AI governance is, including the common principles and pillars to build an AI governance program. This domain covers best practices regardless of industry, sector or size.
I.A Understand what AI is and why it needs governance. (4 to 6 questions)
- Know the generally accepted definitions and types of AI.
- Identify the types of risks and harms posed by AI to individuals, groups, organizations and society (for example misalignment with objectives, ethics and bias risk, and complexity and scalability).
- Identify the unique characteristics of AI that require a comprehensive approach to governance (for example complexity, opacity, autonomy, speed and scale, potential for harm or misuse, data dependency, and probabilistic versus deterministic outputs).
- Identify and apply the common principles of responsible AI (for example fairness, safety and reliability, privacy and security, transparency and explainability, accountability and human-centricity).
I.B Establish and communicate organizational expectations for AI governance. (5 to 7 questions)
- Define roles and responsibilities for AI governance stakeholders.
- Establish cross-functional collaboration in the AI governance program (for example for efficacy and diversity of expertise and perspective).
- Create and deliver a training and awareness program to all stakeholders on AI terminology, strategy and governance.
- Differentiate approaches to AI governance based upon company size, maturity, industry, products and services, objectives and risk tolerance.
- Identify differences among AI developers, providers, deployers and users from a governance perspective (for example with respect to responsibilities, opportunities and needs).
I.C Establish policies and procedures to apply throughout the AI life cycle. (6 to 8 questions)
- Create and implement policies to ensure oversight and accountability across all AI life cycle stages (for example use case assessment, risk management, ethics by design, data acquisition and use, model and system development, training and testing, deployment and monitoring, documentation and reporting, and incident management).
- Evaluate and update existing policies (for example data privacy, security, data governance and intellectual property) for AI.
- Create, update and implement policies, assessments and contracts to manage third-party risk (for example procurement, supply chain, human resources and acceptable use).
Domain II: Understanding how laws, standards and frameworks apply to AI (19 to 23 questions)
Domain II, Understanding how laws, standards and frameworks apply to AI focuses on existing laws that apply to AI, as well as AI-specific laws, standards and frameworks. For the AI governance professional, this means an understanding of the major elements of current AI laws (for example the EU AI Act, the South Korean AI Basic Law, federal and state AI laws that apply to private sector organizations).
II.A Understand how existing data privacy laws apply to AI. (4 to 6 questions)
- Understand how transparency, choice, lawful basis and purpose limitation requirements apply to AI.
- Understand how data minimization and privacy by design requirements apply to AI.
- Understand how obligations on data controllers apply to AI (for example regarding privacy impact assessments, use of third-party processors, cross-border data transfers, data subject rights, automated decision making, incident management, breach notification and record keeping).
- Understand the requirements that apply to sensitive or special categories of data (for example biometrics).
II.B Understand how other types of existing laws apply to AI. (4 to 6 questions)
- Understand how intellectual property laws apply to AI (for example prohibiting or limiting use of data for AI training).
- Understand how nondiscrimination laws apply to AI (for example in the employment, credit, lending, housing and insurance contexts).
- Understand how consumer protection laws apply to AI (for example prohibiting unfair and deceptive acts or practices).
- Understand how product liability laws apply to AI (for example prohibiting design and manufacturing defects).
II.C Understand the main elements of AI-specific laws. (6 to 8 questions)
- Understand the risk classification framework for AI (for example prohibited/high/limited/minimal risk) and what systems/uses fall into each category.
- Understand the key requirements around risk management, data governance, technical documentation, conformity/impact assessments and record keeping.
- Understand the key requirements around human oversight, transparency and notification, and quality management.
- Understand the distinct requirements for general-purpose AI models.
- Understand the enforcement framework and penalties for noncompliance.
- Understand the differences in requirements based on organizational context (for example providers, deployers, importers and distributors).
II.D Understand the main industry standards and tools that apply to AI. (3 to 5 questions)
- Understand the Organisation for Economic Co-operation and Development (OECD) principles, framework, policies and recommended practices for trustworthy AI.
- Understand the NIST AI Risk Management Framework and Playbook (for example core functions, categories and subcategories).
- Understand the core ISO AI standards (that is 22989, 42001 and 42005).
Domain III: Understanding how to govern AI development (21 to 25 questions)
Domain III, Understanding how to govern AI development focuses on the responsibilities of AI governance professionals with respect to designing, building, training, testing and maintaining AI systems.
III.A Govern the designing and building of the AI system. (6 to 8 questions)
- Define the business context and use case of the AI system.
- Perform or review an impact assessment on the AI system.
- Apply the policies, procedures, best practices and ethical considerations to designing and building the AI system (for example purpose of AI, requirements gathering, architecture and model selection, human oversight, data analysis, metric and threshold evaluation, stakeholder engagement and feedback, and operational controls).
- Identify and manage the internal and external risks and contributing factors related to designing and building the AI model and system (for example using probability/severity harms matrix, using a risk mitigation hierarchy, stakeholder mapping, use-case evaluation, benchmarking, and pre-deployment pilots and testing).
- Document the designing and building process (for example to establish compliance and manage risks).
III.B Govern the collection and use of data in training and testing the AI model and system. (6 to 8 questions)
- Establish and follow the requirements for data governance (for example assess and document lawful rights to collect and use data, and assess data quality, quantity, integrity and fit-for-purpose).
- Establish and document data lineage and provenance.
- Plan and perform training and testing of the AI model and system (for example unit, integration, validation, performance, security, bias and interpretability).
- Identify and manage issues and risks during training and testing of an the AI model and system.
- Document the training and testing process (for example to validate results, establish compliance and manage risks).
III.C Govern the release, monitoring and maintenance of the AI system. (8 to 10 questions)
- Assess readiness, and prepare for release into production (for example creating the model card and satisfying conformity requirements).
- Conduct continuous monitoring of the AI system, and establish a regular schedule for maintenance, updates and retraining.
- Conduct periodic activities to assess the AI system’s performance, reliability and safety (for example audits, red teaming, threat modeling and security testing).
- Manage and document incidents, issues and risks.
- Collaborate with cross-functional stakeholders to understand why incidents arise from AI systems (for example brittleness, lack of robustness, lack of quality data, insufficient testing, and model or data drift).
- Make public disclosures to meet transparency obligations (for example technical documentation, instructions for use to deployers and post-market monitoring plans).
Domain IV: Understanding how to govern AI deployment and use (21 to 25 questions)
Domain IV, Understanding how to govern AI deployment and use focuses on the responsibilities of AI governance professionals with respect to selecting an AI model, then deploying and using it responsibly through ongoing monitoring maintenance, and other key obligations. This domain applies in any deployment context, such as a company deploying its own proprietary model or one from a third party.
IV.A Evaluate key factors and risks relevant to the decision to deploy the AI system. (6 to 8 questions)
- Understand the context of the AI use case (for example business objectives, performance requirements, data availability, ethical considerations and workforce readiness).
- Understand the differences in AI model types (for example classic vs. generative, proprietary vs. open source, small vs. large, and language vs. multimodal capabilities).
- Understand the differences in AI deployment options (for example cloud vs. on-premise vs. edge, and using the AI model as is or with fine-tuning, retrieval augmented generation, agentic architectures, or other techniques to improve performance and fit).
IV.B Perform key activities to assess the AI system. (5 to 7 questions)
- Perform or review an impact assessment on the selected AI system.
- Identify and evaluate key terms and risks in the vendor or licensing agreement.
- Identify and understand the risks and opportunities that are unique to a company deploying its own proprietary AI model (for example increased obligations and higher potential liability).
IV.C Govern the deployment and use of the AI system. (9 to 11 questions)
- Apply the policies, procedures, best practices and ethical considerations to the deployment of an AI system (for example data governance, risk management, issue management, and user training).
- Conduct continuous monitoring of the AI model and system, and establish a regular schedule for maintenance, updates and retraining.
- Conduct periodic activities to assess the AI system’s performance, reliability and safety (for example audits, red teaming, threat modeling and security testing).
- Document incidents, issues, risks and post-market monitoring plans.
- Forecast and reduce risks of secondary or unintended uses and downstream harms.
- Establish external communication plans.
- Create and implement a policy and controls to deactivate or localize an AI system as necessary (for example due to regulatory requirements or performance issues).
Who It Is For
Is AIGP the right IAPP credential for you?
The IAPP describes AIGP as our newest certification and says it's about ensuring safety and trust in the development and deployment of ethical AI and the ongoing management of AI systems. It fits anyone responsible for adopting or managing AI in an organization. The AIGP Body of Knowledge puts it this way: "Anyone responsible for adopting or managing AI in an organization will benefit."
Holding a CIPP plus an AIGP, CIPM or CIPT is also one step toward the IAPP's Fellow of Information Privacy designation, which also asks for three years of work experience where privacy is at least half the job, three peer references and a personal statement.
Why the IAPP says to pursue AIGP
- Establish foundational knowledge of AI systems and their use cases, the impacts of AI, and comprehension of responsible AI principles.
- Demonstrate an understanding of how current and emerging laws apply to AI systems, and how major frameworks are capable of being responsibly governed.
- Show comprehension of the AI life cycle, the context in which AI risks are managed, and the implementation of responsible AI governance.
- Presents awareness of unforeseen concerns with AI and knowledge of debated issues surrounding AI governance.
Exam Format
The numbers behind AIGP.
Exam details
- Questions: 100
- Time: 2.75 hours
- Question types: multiple choice, and some are scenario-based
- Multi-select items tell you exactly how many responses to pick, with no partial credit
- Delivery: Pearson VUE test centers or OnVUE online proctoring
- Score scale: 100 to 500, with 300 or above to pass
- Results: shown on screen right after the exam, with a section breakdown by domain
- Purchase window: schedule and take the exam within one year of buying it
The IAPP says scores are based only on the total number of scored questions you answer correctly. There's no weighting and no minimum per section. The cut score comes out of beta testing and psychometric review, and it's set to 300 on the scale. The IAPP is clear that 300 doesn't represent 60%.
Your section breakdown shows the percentage you got right in each blueprint domain. You can't average those to get a total, because the domains don't carry the same number of questions. The IAPP also won't show you which questions you missed.
Study Plan
A practical path to AIGP.
The IAPP tells candidates to plan for at least 30 hours of study, even if you've passed other certification exams. Here's how we spread that time across the blueprint.
We start with Domain I, Understanding the foundations of AI governance. The blueprint sets 16 to 20 questions here across 3 competencies, so we work each performance indicator with a short scenario.
Next we cover Domain II, Understanding how laws, standards and frameworks apply to AI. The blueprint sets 19 to 23 questions here across 4 competencies, so we work each performance indicator with a short scenario.
Then we move to Domain III, Understanding how to govern AI development. The blueprint sets 21 to 25 questions here across 3 competencies, so we work each performance indicator with a short scenario.
We wrap up with Domain IV, Understanding how to govern AI deployment and use. The blueprint sets 21 to 25 questions here across 3 competencies, so we work each performance indicator with a short scenario.
Domain III has the highest question range, so it gets the most review time. In the last week we run timed practice at the real pace, review your weakest domains, then reread the body of knowledge line by line. The IAPP says the body of knowledge should be your core resource.
Exam Policies
What to know before you book AIGP.
You buy the exam in the IAPP store, then schedule through your MyIAPP profile with Pearson VUE. Schedule at least 24 hours ahead. Before you book, make sure the name in MyIAPP matches your government-issued ID exactly.
Test center appointments can be rescheduled or canceled up to 48 hours ahead. OnVUE appointments can be changed up to 15 minutes past the scheduled start. A missed appointment counts as a no-show and uses up the attempt.
OnVUE exams have to run on a personal computer, not a work device, with no VPN and no external monitors. You get a scheduled 15-minute break. Leaving the webcam view at any other time gets the exam revoked.
If you don't pass, you can buy a new exam once your result shows in MyIAPP, and you can't book a date sooner than seven days after your last attempt. The IAPP offers a discounted retake price. Accommodations such as extra time start with the IAPP's Special Testing Accommodation Request Form, which a licensed health care provider completes in part.
Passing isn't the last step. To activate and keep the credential, you meet the IAPP's CPE requirements and either pay the certification maintenance fee of USD250 for a certification term or hold IAPP membership at USD295 a year.
Career Outlook
Where AIGP can take you.
AIGP is common among AI governance leads, risk and compliance managers and the people who run responsible AI programs. BLS doesn't track privacy roles on their own, so we use the closest technology occupation as a reference.
The U.S. Bureau of Labor Statistics reports that the median annual wage for computer and information systems managers was $175,140 in May 2025. Treat it as a reference point and not a promise. Location, experience and the employer drive what any one person earns. Source: BLS Occupational Outlook Handbook.
Funding and PathPay
Ways to pay for AIGP training.
Paying on your own? AIGP prep is eligible for PathPay, our monthly payment plan. Terms of 3, 4 and 6 months carry no added fee. 9, 12 and 18 month terms add 5%, 10% and 15%. Course access opens when your first payment clears.
Our funding page lists every other way to pay, and you can always talk with us or email info@accumentum.net before you enroll.
Frequently Asked Questions
AIGP with Accumentum and IAPP: audience, prerequisites, delivery, and enrollment.
The IAPP Certified AI Governance Professional (AIGP) validates expertise in AI governance frameworks, ethical AI practices, and responsible AI management.
AI Governance Professionals: Individuals responsible for overseeing ethical AI development and deployment within organizations. Data Privacy and Compliance Officers: Professionals seeking to integrate AI governance with privacy and regulatory requirements. Technology and Risk Managers: Those focused on managing AI-related risks, including bias, ethics, and compliance challenges. Aspiring AI Governance Experts: Individuals aiming to earn the AIGP certification to advance their careers in responsible AI management.
Basic Knowledge of AI and Privacy: Familiarity with fundamental AI concepts and privacy principles is recommended but not mandatory. Professional Experience: At least one to two years of experience in AI, data protection, compliance, or governance roles is preferred. Interest in AI Governance: A strong interest in learning about ethical AI practices and regulatory frameworks for AI systems. No Formal Certification Required: No prior certifications are necessary, though a background in AI, IT, or privacy is advantageous.
Ten modules: AI governance, AI technologies and privacy implications, ethical AI, governance frameworks, risk management, privacy by design, regulatory compliance, stakeholder engagement, incident response, and exam-ready case studies.
Interactive lectures, real-world AI governance case studies, group activities, and exam preparation with mock tests.
Earning AIGP positions you for leadership roles in overseeing AI governance initiatives and driving responsible AI strategies.
Not through us right now. Accumentum is an Army CA and Air Force COOL Approved Vendor for courses from AWS, Cisco, CompTIA, EC-Council, HRCI, ISACA, ISC2, Microsoft, PMI and SHRM. IAPP courses aren't part of that approval. PathPay can spread the cost into monthly payments. It's a payment plan, not a loan.
Register through Accumentum's registration page. Select Certified AI Governance Professional (AIGP) Training, your funding option, and how you discovered Accumentum.
The IAPP lists 100 questions with 2.75 hours to finish. Some are scenario-based, and multi-select items tell you how many answers to pick.
300 or higher on the IAPP's 100 to 500 scale. The IAPP notes that 300 doesn't equal 60%.
You can buy a new exam once your result posts in MyIAPP, and you can't schedule sooner than seven days after your last attempt. Retakes get a discounted price.
Yes. Test center appointments can be changed up to 48 hours ahead, and OnVUE appointments up to 15 minutes past the start time. You still need to test within one year of purchase.
Meet the IAPP CPE requirements and either pay the USD250 certification maintenance fee for a term or keep an IAPP membership at USD295 a year.
The IAPP recommends at least 30 hours of study time, even for people who have passed other certification exams.
Yes. AIGP prep is eligible for PathPay. Terms of 3, 4 and 6 months add no fee, and longer terms add 5% to 15%.
Enrollment
Enroll in AIGP Certification Training with Accumentum.
Enroll in the IAPP Certified AI Governance Professional (AIGP) Training Course with Accumentum to advance your AI governance expertise and earn a globally recognized credential. This course is your gateway to becoming a certified AI governance professional aligned with IAPP standards. For detailed information and to secure your spot, visit Accumentum's registration page linked below.
Start AIGP and lead responsible AI governance.
IAPP Certified AI Governance Professional training for AI governance professionals, privacy officers, and technology risk managers.