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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability |
| Topic 2: AI Life Cycle Risk Management | - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation - AI model and data risk identification | |
| Topic 3: AI Risk Program Management | 42% | - AI governance communication and reporting - AI risk assessment and treatment strategies - Enterprise AI risk program design - AI risk monitoring and continuous improvement |
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NEW QUESTION # 55
Which of the following metrics BEST indicates false positives in an AI model output?
Answer: C
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. Precision is directly affected by false positives because it measures the proportion of predicted positives that are actually positive. Recall is more sensitive to false negatives, F1 combines both dimensions, and overall accuracy can hide a high false-positive rate. This makes option A, Precision, 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 # 56
An organization seeks to implement a new AI system that uses customer information to create targeted product recommendations. Which of the following is the MOST important consideration to ensure the system complies with regulatory requirements?
Answer: C
Explanation:
Privacy and data protection regulations worldwide-including GDPR, CCPA, and sector-specific laws- impose strict requirements on the collection, use, and processing of personal information. Customer data used for AI systems must be obtained through lawful means with appropriate consent for the specific processing purpose.
Why A is Correct: According to ISACA AAIR guidance on regulatory compliance, the legal basis for processing personal data is the foundational requirement. An AI system built on data collected without proper consent or legal authorization exposes the organization to regulatory penalties, reputational damage, and forced shutdown of the system. Consent must be specific to the AI use case, not merely generic data collection consent.
Why B is Wrong: Backup and storage protocols address data security and resilience, which are compliance requirements but secondary to the lawfulness of data collection. Securely storing improperly obtained data does not cure the regulatory violation.
Why C is Wrong: Human review of recommendations is a governance safeguard for accuracy and fairness, not a regulatory compliance requirement for data collection. Many regulations do not require human review of recommendation systems.
Why D is Wrong: Supervised learning is a modeling technique that does not address regulatory compliance regarding data sourcing. The training methodology is irrelevant to whether the underlying data was legally obtained.
NEW QUESTION # 57
To reinforce organization-wide ethical norms and risk recognition, which of the following is MOST important to integrate into AI user training?
Answer: D
Explanation:
Effective AI user training must go beyond policy acknowledgment and compliance instruction to equip employees with the practical skills needed to identify ethical risks and report them appropriately. This builds an active risk-aware workforce.
Why B is Correct: The ISACA AAIR framework identifies that training on ethical risk indicators and reporting mechanisms directly reinforces ethical norms by enabling employees to recognize real-world signs of AI misuse, bias, or harmful outputs. When staff can identify specific risk signals and know how to escalate them, the organization builds a proactive risk culture grounded in practical ethical literacy.
Why A is Wrong: Acceptable use policy acknowledgment is a compliance activity, not a culture-building measure. Acknowledging a document does not ensure employees understand how to apply ethical principles in practice.
Why C is Wrong: Cyber threat identification addresses security risk, which is narrower than the full scope of ethical AI risk. Security training does not develop ethical judgment regarding fairness, bias, or societal impact.
Why D is Wrong: Regulatory compliance checklists address legal obligations but do not develop the ethical reasoning and risk recognition skills needed to reinforce organizational norms.
NEW QUESTION # 58
Which of the following MOST effectively reduces bias resulting from variances in free-form answers in the dataset?
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. Free-form data can contain inconsistent formats, scales, terminology, and representations that distort model learning.
Scaling and standardizing inputs reduces unwanted variance and helps prevent irrelevant formatting differences from becoming sources of bias. This makes option C, Scaling and standardizing inputs, 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 # 59
Risk practitioners use automated tools to generate potential AI risk scenarios. Which of the following represents the GREATEST risk from that approach?
Answer: B
Explanation:
Automated risk scenario generation tools operate based on programmed logic, historical data, and pattern recognition. They may excel at generating scenarios based on known risks and documented processes but struggle to account for complex organizational interdependencies that are not fully captured in their data inputs.
Why D is Correct: The ISACA AAIR risk scenario development guidance identifies the failure to account for process interdependencies as the greatest risk from automated scenario generation. AI systems do not operate in isolation-they are embedded in complex organizational ecosystems where failures cascade through interconnected processes, systems, and stakeholders. Automated tools may miss these interdependencies, producing scenarios that are technically accurate in isolation but miss the most consequential cascade effects.
Why A is Wrong: Complexity in likelihood and impact scoring is a risk quantification challenge that affects scenario prioritization but does not result in missing scenarios entirely. Complex scoring can be managed through additional analytical methods.
Why B is Wrong: Emerging adversarial attack vectors are a potential blind spot for any tool or analyst working from historical data, but this is a known limitation of retrospective approaches that can be supplemented with threat intelligence. It does not represent the distinctive risk of automated scenario generation.
Why C is Wrong: Underestimating model change impacts is a scenario calibration issue that represents a less severe risk than missing entire categories of scenarios arising from unmodeled interdependencies.
NEW QUESTION # 60
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