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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | - AI model and data risk identification - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks | |
| Topic 2: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability |
| Topic 3: AI Risk Program Management | 42% | - AI risk monitoring and continuous improvement - Enterprise AI risk program design - AI governance communication and reporting - AI risk assessment and treatment strategies |
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NEW QUESTION # 172
Which of the following is the MOST important consideration when determining mitigation controls for an AI system?
Answer: B
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 # 173
Which of the following is the MOST important consideration when selecting an AI governance framework for a civil aviation system?
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. Civil aviation is safety-critical, so governance must preserve qualified human oversight, intervention, escalation, and override capability. Technical simplification, privacy, and centralization are relevant but do not replace human accountability for consequential decisions. This makes option A, Provisions for human oversight, 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 # 174
An organization plans to deploy AI for customer service automation. Which of the following governance approaches BEST aligns culture and risk tolerance?
Answer: D
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. Governance should scale controls to the impact of the customer-service AI system and involve business stakeholders so the control environment reflects risk tolerance and gains operational support. Monitoring alone does not create cultural alignment. This makes option C, Scale controls to AI system impact and involve business stakeholders to obtain buy-in, 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 # 175
A social media platform uses AI tools to automate real-time content moderation. Which of the following would be of GREATEST concern to a risk practitioner?
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. Automated content moderation can falsely restrict users, so a meaningful appeal process is essential for contestability, human review, and correction of erroneous decisions. Compute cost and reinforcement learning are not the primary governance concern. This makes option D, Lack of an appeal process for users falsely accused of violating terms of service, 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 # 176
An organization deploys an AI credit scoring model trained on historical financial data that underrepresents certain demographic groups. Which of the following is the risk practitioner's BEST recommendation to mitigate this risk?
Answer: A
Explanation:
Bias in AI models often originates from training data that does not represent the full population the model will serve. Underrepresentation of demographic groups in training data causes the model to perform poorly for those groups, producing discriminatory outcomes in high-stakes decisions like credit scoring.
Why B is Correct: The ISACA AAIR bias and fairness guidance identifies expanding training data coverage as the most effective mitigation for representation bias. Defining specific inclusivity goals ensures the data expansion targets the identified gaps, while broadening data sources introduces representative examples from underrepresented groups. This addresses the root cause-training data deficiency-rather than symptoms.
Why A is Wrong: Model drift reporting detects changes in model behavior over time but does not address existing representational bias embedded in the current model. Monitoring an already-biased model cannot remediate the bias.
Why C is Wrong: Notifying stakeholders of potential inaccuracy is a transparency measure but does not reduce harm to affected individuals. Disclosure of bias without remediation is insufficient under anti- discrimination regulations.
Why D is Wrong: Unsupervised learning can identify hidden patterns but cannot introduce the missing representative data needed to train an unbiased model. Discovering discriminatory patterns in existing data does not resolve the underlying data coverage gap.
NEW QUESTION # 177
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