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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Governance and Framework Integration | 37% | - AI Policies, Procedures, and Organizational Training - AI Regulatory Compliance and Legal Considerations - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases - AI Trustworthiness, Ethical and Societal Implications |
| AI Risk Program Management | 42% | - AI Risk Monitoring and Reporting - AI Risk Identification and Assessment - AI Risk Response and Mitigation - AI Risk Assurance and Continuous Improvement |
| AI Life Cycle Risk Management | 21% | - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation - AI Data and Asset Management - AI Design, Development/Procurement, and Documentation |
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NEW QUESTION # 48
Which of the following is a risk practitioner's BEST justification for embedding AI risk considerations into acceptable use policies?
Answer: A
Explanation:
Acceptable use policies (AUPs) govern how employees interact with organizational systems and tools.
Embedding AI risk considerations into AUPs ensures that AI-related behaviors align with the organization's risk appetite and tolerance thresholds.
Why C is Correct: According to ISACA AAIR governance principles, the best justification for embedding AI risk in AUPs is maintaining consistent enterprise risk tolerance across all AI-driven decision-making. When risk tolerances are codified in AUPs, employees understand what AI behaviors are permissible, and deviation from these boundaries triggers escalation. This enterprise-wide alignment prevents individual business units from accepting risks that exceed organizational thresholds.
Why A is Wrong: Shadow AI mitigation through allow lists is a specific technical control mechanism, not the primary governance justification for AUP integration. It addresses unauthorized tool use rather than risk tolerance alignment.
Why B is Wrong: Applying uniform risk controls across diverse business functions is a compliance approach that may not be appropriate-different functions may legitimately have different risk profiles. The goal is tolerance alignment, not control uniformity.
Why D is Wrong: Assigning accountability to business unit leadership is a governance structure decision.
AUPs define behavioral expectations, not organizational accountability assignments, which are addressed through RACI frameworks and policy governance.
NEW QUESTION # 49
Which of the following is the PRIMARY benefit of adopting an AI governance framework?
Answer: B
Explanation:
Within the ISACA Advanced in AI Risk framework, governance decisions should align AI use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal or ethical obligations. An AI governance framework primarily creates structured enterprise oversight, accountability, policy alignment, monitoring, and compliance. Resource allocation and technical standardization may improve, but they are secondary outcomes rather than the central governance benefit. This makes option A, Structured enterprise AI risk oversight and compliance, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 50
Which of the following is the MOST suitable key risk indicator (KRI) for model drift resulting in biased or discriminatory outputs?
Answer: B
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. A disparate impact ratio directly measures differences in favorable outcomes across groups and is therefore the most relevant KRI when the stated concern is discriminatory output. Population stability measures distributional drift but does not directly establish disparate treatment. This makes option C, Disparate impact ratio, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 51
Which of the following is MOST important for a risk practitioner to verify when reviewing the decommissioning process for a customer-facing AI solution?
Answer: B
Explanation:
Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems.
Decommissioning a customer-facing AI solution must include secure disposal of personally identifiable information that no longer has a valid retention purpose. Encryption and API revocation are useful while assets remain active, but they do not remove end-of-life privacy exposure. This makes option D, Secure disposal of personally identifiable information (PII), the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 52
An organization plans to deploy a generative AI system that processes sensitive personal data across multiple countries with varying privacy laws. Which of the following is the BEST course of action to manage legal and regulatory exposure?
Answer: A
Explanation:
Multi-jurisdictional AI deployment requires jurisdiction-specific compliance strategies because privacy and data protection laws vary significantly across countries. A one-size-fits-all approach frequently fails to meet local requirements, while post-deployment remediation creates legal exposure during the gap period.
Why B is Correct: According to ISACA AAIR guidance, the best approach to multi-jurisdictional compliance is to tailor controls to each relevant statutory framework before deployment and maintain audit trails that demonstrate adherence. This proactive, documented approach reduces legal exposure, satisfies regulatory examination requirements, and enables the organization to demonstrate accountability-a key requirement of frameworks like GDPR.
Why A is Wrong: Post-deployment remediation means the organization is non-compliant during deployment, which creates immediate regulatory exposure. Iterative fixes after harm has occurred are inadequate for protecting individuals or the organization.
Why C is Wrong: Uniform global policies cannot satisfy jurisdictions with conflicting requirements-some laws mandate data residency within borders, making cross-border transfer impossible regardless of encryption strength.
Why D is Wrong: Restricting disclosure of model operations conflicts with transparency requirements embedded in many privacy laws, including GDPR's right to explanation. IP protection cannot override regulatory disclosure obligations.
NEW QUESTION # 53
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