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| Section | Objectives |
|---|---|
| Topic 1: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 2: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 3: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 4: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Topic 5: AI Risk Management | - Risk identification and assessment for AI systems
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NEW QUESTION # 40
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: D
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 # 41
An organization uses an AI model that learns from live data streams. Which of the following is the BEST course of action to manage the risk of an adaptive model?
Answer: A
Explanation:
AI models that learn from live data streams continuously update their parameters based on incoming data.
This creates two specific risks: the model's behavior may drift from its validated state as data patterns change (data drift), and adversaries may deliberately introduce malicious data to manipulate the model's learning (data poisoning).
Why D is Correct: According to ISACA AAIR adaptive model risk guidance, implementing automated monitoring for both data drift and data poisoning is the most comprehensive response to live-learning model risks. Automated monitoring operates continuously at the speed of the data stream, detecting statistical changes in input distributions (drift signals) and anomalous data patterns (poisoning signals) in real time- enabling timely intervention before either risk materializes into harmful behavior.
Why A is Wrong: Defense-in-depth for model access controls who can interact with the model but does not address risks arising from the data the model learns from. Access controls are necessary but insufficient for managing adaptive learning risks.
Why B is Wrong: Restricting data sources reduces learning breadth, potentially undermining the model's adaptive capability that creates its value. Periodic inspections are too infrequent for live-learning systems where risks can emerge between inspection cycles.
Why C is Wrong: Dynamic performance thresholds detect output degradation after drift has occurred. While useful as a safety net, this reactive monitoring does not prevent drift or detect poisoning early enough for the live-learning risk context.
NEW QUESTION # 42
A risk practitioner learns that a credit-scoring AI system is exhibiting bias that cannot be eliminated through further training. Which of the following is the risk practitioner's BEST recommendation?
Answer: D
Explanation:
Credit scoring AI systems are subject to anti-discrimination regulations that prohibit using models that produce biased outcomes affecting protected classes. When bias cannot be eliminated through technical means, continuing to operate the system creates ongoing legal violations and harm to affected individuals.
Why B is Correct: According to ISACA AAIR risk treatment guidance and legal compliance obligations, removing a biased credit-scoring system from production is the appropriate response when bias cannot be technically remediated. Continuing to operate a system known to produce discriminatory credit decisions violates anti-discrimination laws (such as the Equal Credit Opportunity Act), exposes the organization to regulatory enforcement, and causes ongoing harm to affected borrowers. Risk avoidance through system withdrawal is the appropriate treatment when the risk cannot be adequately mitigated.
Why A is Wrong: Requesting senior management risk acceptance for confirmed legal violations is inappropriate because organizations cannot accept risks involving known regulatory breaches. Senior management cannot legitimately authorize continued discriminatory lending practices.
Why C is Wrong: Sourcing a replacement system is a necessary future action but takes time to procure, validate, and deploy. In the interim, the biased system should not continue operating. Removing the system from production should precede replacement planning.
Why D is Wrong: Applying compensating controls to generate offsetting biases compounds the discriminatory problem rather than resolving it. Deliberately introducing additional bias-even in the opposite direction-creates an unpredictably biased model that does not produce fair outcomes.
NEW QUESTION # 43
An organization adopts a third-party AI service under a shared responsibility model. Which of the following is the MOST important area of focus for the risk practitioner?
Answer: C
Explanation:
The shared responsibility model creates complexity in AI governance because control obligations are distributed between the organization and the vendor. The most critical risk is ambiguity about who owns specific controls and who makes decisions when issues arise.
Why D is Correct: The ISACA AAIR framework identifies documented assignment of control ownership as the cornerstone of shared responsibility governance. Without explicit documentation of which controls the organization owns versus which the vendor owns, and who has decision authority in each scenario, gaps and overlaps emerge that allow risks to go unmanaged. Named ownership ensures accountability persists across the shared boundary.
Why A is Wrong: Staff training on procedures is important but addresses operational readiness rather than the fundamental governance challenge of shared responsibility. Training supports a well-structured model but cannot substitute for defined ownership.
Why B is Wrong: Contractual liability clauses are legal protections that determine financial recourse after incidents. While essential, they do not prevent governance gaps from forming during normal operations.
Why C is Wrong: Data pathway testing is a security assurance activity addressing technical controls. It verifies control function but does not establish who owns those controls or what authority they have in the shared model.
NEW QUESTION # 44
Which of the following is the MOST important consideration when determining mitigation controls for an AI system?
Answer: C
Explanation:
Control selection for AI systems requires balancing the effectiveness and cost of proposed controls against the potential losses or harms the controls are designed to prevent. This cost-benefit analysis ensures resources are allocated proportionately to risk reduction value.
Why C is Correct: The ISACA AAIR control selection guidance identifies the cost-benefit analysis of control effectiveness versus potential business losses as the most important mitigation control determination factor.
Implementing controls that cost more than the risk they mitigate represents inefficient risk management; failing to implement cost-effective controls that prevent large losses represents inadequate risk management.
This proportionality assessment is the foundation of risk-based control selection.
Why A is Wrong: Risk awareness training is an important enabler of effective risk management but is an organizational capability development activity rather than a control selection criterion. Training supports controls but does not determine which controls to implement.
Why B is Wrong: Control performance baselines and compliance reporting requirements are governance and compliance management activities. While necessary for control monitoring, they describe how controls are measured after selection, not how controls are selected in the first place.
Why D is Wrong: Computational complexity is a technical characteristic of the AI system that influences implementation considerations but is not the primary driver of control selection. The most computationally complex system still requires controls proportionate to its risk profile, not its technical architecture.
NEW QUESTION # 45
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