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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Trustworthiness, Ethical and Societal Implications - AI Regulatory Compliance and Legal Considerations - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment |
| Topic 2: AI Risk Program Management | 42% | - AI Risk Assurance and Continuous Improvement - AI Risk Response and Mitigation - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting |
| Topic 3: 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 # 46
Which of the following BEST mitigates privacy exposure when using datasets for AI model training that include personal information?
Answer: D
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. De- identification directly reduces privacy exposure by removing or transforming identifying information before personal data is used for training. Authentication and human review protect access or oversight but do not remove personal information from the dataset itself. This makes option C, Implementing robust de- identification controls, 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 # 47
Which of the following poses the GREATEST challenge when performing root cause analysis for incidents involving AI systems and data?
Answer: A
Explanation:
Root cause analysis for AI incidents requires the ability to trace system behavior back through decision logic, data processing steps, and model internals to identify what caused the incident. AI systems-particularly deep learning models-often operate as black boxes, making this tracing extremely difficult.
Why A is Correct: According to ISACA AAIR incident management guidance, the lack of transparency in AI systems is the greatest root cause analysis challenge. When decision logic cannot be inspected, when data lineage is unclear, or when model internals are opaque, analysts cannot determine why the system behaved as it did. This transparency deficit prevents accurate root cause identification, perpetuates recurrence, and makes it impossible to demonstrate corrective action to regulators.
Why B is Wrong: Unclear system objectives represent a design and governance problem that should be addressed before deployment. While unclear objectives can contribute to incidents, they are typically knowable and addressable. Lack of transparency during an incident is a more immediate analytical barrier.
Why C is Wrong: Automation bias-the tendency to over-trust automated systems-is a human factors risk that affects decision-making during normal operations. While it may contribute to incidents, it is a behavioral phenomenon rather than the primary technical barrier to root cause analysis.
Why D is Wrong: Privacy compliance requirements may restrict access to certain data needed for analysis, creating constraints on investigation. However, these are governance constraints that can often be addressed through appropriate authorization, not fundamental analytical barriers.
NEW QUESTION # 48
An organization deploys an autonomous system that makes decisions affecting compliance with regulations.
If those decisions could potentially produce regulatory breaches, which of the following BEST helps to manage associated liability exposures?
Answer: C
NEW QUESTION # 49
Which of the following is the GREATEST risk when an AI model is trained on multiple data sources that differ in update frequency and quality?
Answer: A
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. Training from sources with different update frequencies and quality can introduce conflicting, stale, or incomplete information, leading to inconsistent model outputs and operational decisions. Cost and integration issues are secondary to unreliable decision quality. This makes option A, Inconsistency in model outputs and operational decisions, 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 GREATEST concern when an organization cannot clearly explain an AI system's decision-making process and the origin of its inputs?
Answer: C
Explanation:
Explainability and input transparency are foundational requirements for responsible AI governance. When these are absent, organizations lose the ability to identify when AI systems produce harmful, biased, or inaccurate results-leaving those harms undetected and unaddressed.
Why C is Correct: According to ISACA AAIR, the inability to explain AI decisions is most dangerous because it creates an environment where discriminatory or inaccurate outputs can persist undetected. This exposes the organization to regulatory penalties (particularly under anti-discrimination, financial services, and privacy laws), reputational damage, and harm to affected individuals. The detection gap-not knowing what the system is doing wrong-is the core governance failure.
Why A is Wrong: External provider dependence is a third-party risk management concern. While relevant, it is a structural risk that can be addressed through contract management, not an immediate consequence of lacking explainability.
Why B is Wrong: Declining adoption rates represent a change management and trust concern. Business unit reluctance to adopt AI is a cultural and operational issue, not the primary risk from unexplainable AI decisions.
Why D is Wrong: Manual review bottlenecks represent operational inefficiency. They may result from lack of confidence in AI outputs but do not represent the primary organizational harm from unexplainability.
NEW QUESTION # 51
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