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Exam Information
Official specifications published by IAPP
Exam Format
180 min
100
300 out of 500
Professional
Registration
Pearson VUE or online proctoring
English
Validity
2 years
Submit 20 Continuing Privacy Education (CPE) credits; Pay certification maintenance fee ($250 for non-members); Complete CPE credits corresponding to AIGP Body of Knowledge
AIGP Exam Topics and Domains
AIGP is organized into 4 weighted domains.
1
Understanding the foundations of AI governance
[object Object]%
Understand what AI is and why it needs governance
AI Definitions and TypesAI Risks and HarmsUnique AI CharacteristicsResponsible AI Principles
- Know the generally accepted definitions and types of AI
- Identify the types of risks and harms posed by AI
- Identify the unique characteristics of AI that require comprehensive governance
- Identify and apply the common principles of responsible AI
Establish and communicate organizational expectations for AI governance
Roles and ResponsibilitiesCross-functional CollaborationTraining and AwarenessGovernance DifferentiationDeveloper vs Deployer vs User
- Define roles and responsibilities for AI governance stakeholders
- Establish cross-functional collaboration in the AI governance program
- Create and deliver a training and awareness program
- Differentiate approaches to AI governance based on context
- Identify differences among AI developers, deployers and users
Establish policies and procedures to apply throughout the AI life cycle
Oversight and Accountability PoliciesData Privacy and SecurityThird-party Risk Management
- Create and implement policies to ensure oversight and accountability
- Evaluate and update existing data privacy and security policies for AI
- Create and implement policies to manage third-party risk
2
Understanding how laws, standards and frameworks apply to AI
[object Object]%
Understand how existing data privacy laws apply to AI
Notice, Choice, and ConsentData Minimization and Privacy by DesignData Controller ObligationsSensitive Data CategoriesIntellectual Property
- Understand how notice, choice, consent, and purpose limitation apply to AI
- Understand how data minimization and privacy by design requirements apply to AI
- Understand how obligations on data controllers apply to AI
- Understand requirements for sensitive or special categories of data
- Understand how intellectual property laws apply to AI
Understand how other types of existing laws apply to AI
Non-discrimination LawsConsumer ProtectionProduct Liability
- Understand how non-discrimination laws apply to AI
- Understand how consumer protection laws apply to AI
- Understand how product liability laws apply to AI
Understand the main elements of the EU AI Act
Risk Classification FrameworkRequirements by Risk LevelGeneral Purpose AI ModelsEnforcement and PenaltiesOrganizational Context
- Understand the risk classification framework for AI
- Understand key requirements for each risk level
- Understand distinct requirements for general purpose AI models
- Understand enforcement framework and penalties
- Understand differences based on organizational context
Understand the main industry standards and tools that apply to AI
OECD PrinciplesNIST FrameworksISO Standards
- Understand the OECD principles and framework
- Understand the NIST AI Risk Management Framework and tools
- Understand the core ISO AI standards
3
Understanding how to govern AI development
[object Object]%
Govern the designing and building of the AI model
Business Context and Use CaseImpact AssessmentDesign and Build GovernanceRisk ManagementDocumentation
- Define the business context and use case of the AI model
- Perform or review an impact assessment
- Identify laws that apply to the AI model
- Apply policies and best practices to designing and building
- Identify and manage risks related to design and build
- Document the designing and building process
Govern the collection and use of data in training and testing the AI model
Data Governance RequirementsData Lineage and ProvenanceTraining and TestingIssue and Risk ManagementProcess Documentation
- Establish and follow requirements for data governance
- Establish and document data lineage and provenance
- Plan and perform training and testing of the AI model
- Identify and manage issues and risks during training and testing
- Document the training and testing process
Govern the release, monitoring and maintenance of the AI model
Release PreparationContinuous MonitoringPerformance AssessmentIncident ManagementTransparency Obligations
- Assess readiness and prepare for release into production
- Conduct continuous monitoring and establish maintenance schedules
- Conduct periodic activities to assess performance, reliability and safety
- Manage and document incidents, issues and risks
- Collaborate to understand why incidents arise
- Make public disclosures to meet transparency obligations
4
Understanding how to govern AI deployment and use
[object Object]%
Evaluate key factors and risks relevant to the decision to deploy the AI model
Use Case ContextAI Model TypesDeployment Options
- Understand the context of the AI use case
- Understand differences in AI model types
- Understand differences in AI deployment options
Perform key activities to assess the AI model
Model AssessmentVendor and License ManagementProprietary Model Deployment
- Perform or review an impact assessment on the selected AI model
- Identify laws that apply to the AI model
- Identify and evaluate key terms and risks in agreements
- Understand issues unique to deploying proprietary models
Govern the deployment and use of the AI model
Deployment GovernanceMonitoring and MaintenancePeriodic Assessment ActivitiesDocumentation and Risk ManagementSecondary Use and Downstream HarmsCommunication and Deactivation
- Apply policies, procedures, best practices and ethical considerations to deployment
- Conduct continuous monitoring and establish maintenance schedules
- Conduct periodic activities to assess performance, reliability and safety
- Document incidents, issues, risks and post-market monitoring
- Forecast and reduce risks of secondary uses and downstream harms
- Establish external communication plans
- Create and implement policies to deactivate or localize AI models
How do I earn this certification?
Passing AIGP earns the AIGP - Artificial Intelligence Governance Professional certification. It sits in the AI Governance track.
Next Level Options
Alternative Paths
- CIPP/CN - Chinese Personal Information ProtectionChina AI and data regulations
- CIPP/C - Canadian PrivacyCanadian AI governance requirements
- CIPP/A - Asia Pacific PrivacyAPAC AI governance landscape
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
Use the official PlanetCert Practice Test alongside the study plan below to prepare efficiently for AIGP.
What's changed on this exam?
Current Status
- ACTIVE
- Last content update: 2025-02-03
- Announcement date: 2024-11-01
Updates
- Large Language Models GPT-4 and beyond Increased focus on LLM governance in exam content • Release date: 2024-2025
- Retrieval Augmented Generation (RAG) Production implementations New questions on RAG governance and data management • Release date: 2024-2025
- AI Red Teaming Standardized approaches Red teaming now part of assessment activities • Release date: 2024
Who should take this exam?
This exam is typically taken by AI Governance Professionals and Privacy Professionals.
- Experience in privacy, data protection, or AI governance
- Understanding of AI technologies and their applications
- Familiarity with regulatory compliance
- Knowledge of risk management principles