Artificial Intelligence Governance Professional - AIGP
Security Governance and Responsible AI
Apply security, privacy, compliance, and responsible AI controls to exam scenarios.
Official Scope and Verification
This lesson is mapped to the verified Artificial Intelligence Governance Professional - AIGP outline. Official sources and public status were rechecked on 2026-07-13. Provider pages remain authoritative for late-breaking blueprint, availability, scheduling, price, language, delivery, and retake changes.
Current IAPP AIGP certification. IAPP AIGP Body of Knowledge v2.1, effective 2026-02-02, publishes question-count ranges, competencies, and performance indicators rather than scored percentages.
Official Objectives Emphasized Here
| Domain or objective area | Published weight | Key objective groups | Official source |
|---|---|---|---|
| Understanding the Foundations of Artificial Intelligence Governance | Published without a scored percentage | Understand what AI is and why it needs governance; Establish and communicate organizational expectations for AI governance; Establish policies and procedures to apply throughout the AI life cycle | IAPP official AIGP body of knowledge |
| Understanding How Laws, Standards and Frameworks Apply to AI | Published without a scored percentage | Understand how existing data privacy laws apply to AI; Understand how other types of existing laws apply to AI; Understand the main elements of AI-specific laws; Understand the main industry standards and tools that apply to AI | IAPP official AIGP body of knowledge |
| Understanding How to Govern AI Development | Published without a scored percentage | Govern the designing and building of the AI system; Govern the collection and use of data in training and testing the AI model and system; Govern the release, monitoring and maintenance of the AI system | IAPP official AIGP body of knowledge |
| Understanding How to Govern AI Deployment and Use | Published without a scored percentage | Evaluate key factors and risks relevant to the decision to deploy the AI system; Perform key activities to assess the AI system; Govern the deployment and use of the AI system | IAPP official AIGP body of knowledge |
Authoritative Sources for This Scope
- IAPP official AIGP body of knowledge - Official source; accessed 2026-07-13.
Security, governance, and responsible AI questions ask whether the solution can be trusted, controlled, and explained. For Artificial Intelligence Governance Professional - AIGP, treat governance as part of the design, not a separate cleanup task after the model works.
Controls To Recognize
| Control area | What it protects | What to look for in a scenario |
|---|---|---|
| Identity and access | Systems, documents, tools, models, and administrative actions. | Least privilege, role-based access, service identities, approval boundaries, and separation of duties. |
| Data protection | Training data, prompts, uploaded files, retrieved documents, logs, and outputs. | Classification, encryption, masking, retention, residency, and deletion requirements. |
| Output quality and safety | Users, customers, business decisions, and public trust. | Grounding, citations, evaluations, content filters, policy checks, and human review. |
| Responsible AI | Fairness, transparency, accountability, and social impact. | Bias testing, explainability, consent, documentation, stakeholder review, and appeal paths. |
| Auditability | Evidence that the system was governed and operated responsibly. | Logs, versioning, approvals, risk registers, control tests, and incident records. |
Provider-Specific Risk Lens
Govern data protection, transparency notices, vendor due diligence, DPIAs or impact assessments, human oversight, and incident response.
For IAPP, a governance answer is strongest when it uses the credential's risk language, control vocabulary, lifecycle model, and evidence expectations instead of vague statements like "be ethical" or "monitor the model."
Track-Specific Risk Checks
- privacy leakage through prompts, files, logs, retrieved documents, or generated outputs
- hallucinated or ungrounded answers used without review
- unclear accountability when an AI recommendation affects people, money, security, or compliance
- missing AI owner
- unreviewed high-impact use case
- weak evidence for control effectiveness
- vendor or model change without reassessment
Responsible AI Scenario Checklist
- Purpose: Is the use case appropriate, useful, and clearly bounded?
- People: Who is affected, who can challenge the output, and who owns the decision?
- Data: Was the data collected, used, stored, and shared appropriately?
- Model behavior: Are hallucination, bias, toxicity, privacy leakage, and misuse tested?
- Operations: Are monitoring, incident response, change control, and retirement plans defined?
Example: Prompt Injection And Data Leakage
Scenario: an AI assistant can read internal knowledge articles and call workflow tools. A user tries to make it ignore its instructions and reveal restricted information. The best answer is not just 'write a better prompt.' It should combine access control, tool permission limits, input and output filtering, retrieval permissions, logging, testing, and human escalation for sensitive actions.
How To Study Governance
- Write one governance control for each lifecycle stage: design, data, build, test, deploy, monitor, and retire.
- Practice rejecting answers that rely on user trust, prompt wording, or policy documents without enforcement.
- Use NIST AI RMF and OWASP GenAI security resources as general reference points, then map them back to the provider-specific credential objectives.
Useful Links
- IAPP AIGP - Official Artificial Intelligence Governance Professional page.
- IAPP AI Governance Resources - Official IAPP AI governance resource hub.
- NIST AI Risk Management Framework - General reference for AI risk management practices.
- OWASP GenAI Security Project - General reference for LLM and GenAI application risks.