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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Program Management | 42% | - AI Risk Response and Mitigation - AI Risk Identification and Assessment - AI Risk Assurance and Continuous Improvement - AI Risk Monitoring and Reporting |
| Topic 2: AI Life Cycle Risk Management | 21% | - AI Implementation, Maintenance, and Decommissioning - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation - AI Data and Asset Management |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment - AI Regulatory Compliance and Legal Considerations - AI Ownership, Oversight, and Accountability - AI Policies, Procedures, and Organizational Training - AI Trustworthiness, Ethical and Societal Implications |
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NEW QUESTION # 96
Which of the following is the BEST approach when using AI-driven security ratings to monitor third-party risk?
Answer: A
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. Third-party security ratings are useful only when they support business and compliance decisions.
Mapping rating metrics to strategic objectives and obligations turns technical measures into meaningful risk information instead of isolated performance or availability indicators. This makes option C, Map risk metrics to strategic objectives and compliance obligations, 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 # 97
A credit-scoring AI solution exhibits steadily declining accuracy despite unchanged input distributions.
Which of the following should a risk practitioner consider to be the GREATEST risk?
Answer: D
Explanation:
When an AI model's accuracy declines despite stable input distributions, the most likely cause is concept drift-where the underlying relationship between inputs and the target variable changes over time. In credit scoring, this may occur when economic conditions, consumer behavior, or risk patterns shift in ways not captured in the original training data.
Why C is Correct: The ISACA AAIR model drift guidance identifies concept drift as the greatest risk in this scenario because it means the model is making credit decisions based on relationships that no longer hold in the current environment. Faulty credit decisions can lead to incorrect denials of creditworthy applicants, incorrect approvals of high-risk applicants, regulatory violations, financial losses, and harm to individuals- all high-severity consequences for a credit-scoring application.
Why A is Wrong: Technical delays in credit score updates are an operational performance concern. Delays create business friction but do not cause the fundamental accuracy problem described in the scenario.
Why B is Wrong: Underfitting from shortened training cycles is a model development quality issue. The scenario specifies stable input distributions and declining accuracy-characteristic of drift, not underfitting, which would manifest differently.
Why D is Wrong: Increased retraining costs represent a financial efficiency concern. While budgetary impacts are real, they are secondary to the risk of faulty credit decisions affecting individuals and regulatory compliance.
NEW QUESTION # 98
Which of the following poses the GREATEST risk during the development phase of an AI system?
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. Data poisoning can corrupt the learning process before deployment and embed harmful or biased behavior into the resulting model. Because it attacks training-data integrity directly, inadequate poisoning mitigation is a major development-phase risk. This makes option A, Inadequate mitigation of data poisoning attacks, 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 # 99
Which of the following is the PRIMARY risk reduction benefit of embedding secure coding practices throughout the AI development life cycle?
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. Secure coding throughout the AI development lifecycle reduces exploitable software and integration vulnerabilities before deployment. It does not directly detect model drift, eliminate human review, or guarantee predictive accuracy. This makes option B, Fewer model vulnerabilities that can be exploited by malicious actors, 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 # 100
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 # 101
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