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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | - AI bias, drift, transparency, and control evaluation - AI model and data risk identification - AI development, deployment, and monitoring risks | |
| Topic 2: 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 3: AI Risk Program Management | 42% | - AI governance communication and reporting - AI risk monitoring and continuous improvement - AI risk assessment and treatment strategies - Enterprise AI risk program design |
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NEW QUESTION # 147
An election oversight body is considering the use of AI to identify irregularities in voting patterns. Which of the following is the MOST important risk to evaluate?
Answer: B
Explanation:
AI systems trained on historical data inherit the biases, patterns, and structural inequities embedded in that data. In electoral contexts, historical voting patterns may reflect systemic disenfranchisement, gerrymandering, or demographic manipulation-biases that an AI system could amplify and legitimize through its outputs.
Why B is Correct: According to ISACA AAIR bias and fairness guidance applied to high-stakes public sector AI, the amplification of historical data biases poses the greatest risk in electoral irregularity detection. If the AI system treats historically suppressed voting patterns as the normal baseline, it may flag legitimate turnout increases in previously underrepresented communities as irregularities-producing discriminatory, biased outputs with severe democratic consequences.
Why A is Wrong: Voter location identification is a privacy concern but represents a specific data element risk.
Comprehensive privacy controls can mitigate location exposure without resolving the systemic bias risk.
Why C is Wrong: Contextual drift-the model performing differently in new electoral contexts than in training contexts-is a technical risk that is relevant but addressable through validation testing. Bias amplification is a more fundamental concern embedded in the historical data itself.
Why D is Wrong: Political distrust of AI represents a stakeholder acceptance challenge. While significant for implementation success, it is a communication and change management concern rather than the primary technical and ethical risk from the AI system itself.
NEW QUESTION # 148
An organization intends to implement an AI system that poses significant societal risk and interfaces with critical infrastructure and public services. Which of the following is the BEST course of action?
Answer: D
Explanation:
High-risk AI systems-particularly those affecting critical infrastructure and public services-require rigorous pre-deployment assessment to identify potential harms, regulatory obligations, and societal impacts before they affect people or essential services.
Why A is Correct: The ISACA AAIR framework, consistent with emerging AI regulations (including the EU AI Act's requirements for high-risk systems), mandates comprehensive pre-launch impact assessment for systems posing significant societal risk. This assessment must cover adverse impact scenarios, applicable compliance obligations, and mitigation measures. Acting before deployment prevents irreversible harm and demonstrates responsible governance to regulators and the public.
Why B is Wrong: External consultants can support impact assessment but cannot substitute for the organization's own comprehensive evaluation and accountability. External expertise supplements internal assessment; it does not replace the organization's obligation to assess and take responsibility.
Why C is Wrong: Restricting disclosure conflicts with regulatory transparency requirements for high-risk AI systems. Many jurisdictions require explainability and disclosure for systems affecting public services. IP protection cannot override public safety obligations.
Why D is Wrong: Parallel model evaluation is a technical testing method that quantifies operational performance. It does not constitute the comprehensive societal impact and compliance assessment required for high-risk deployment.
NEW QUESTION # 149
Which of the following approaches is MOST effective for embedding management of AI risk into existing organizational processes and decision-making?
Answer: A
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. AI risk is best embedded by integrating governance checkpoints into established project, change, and risk-review cycles. This ensures AI considerations are part of normal decision-making instead of being handled by a parallel process that can become isolated from business governance. This makes option B, Integrate AI governance into established project and risk review cycles, 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 # 150
A risk practitioner is performing a post-implementation review for an AI system used for credit scoring.
Which of the following is MOST important for the risk practitioner to confirm?
Answer: D
Explanation:
Credit scoring AI systems make high-stakes financial decisions that directly affect individuals' access to credit. Post-implementation review for such systems must confirm that the system performs within ethical, legal, and regulatory boundaries-particularly regarding fairness and explainability.
Why B is Correct: According to ISACA AAIR post-implementation review guidance for high-stakes AI, confirming explainability and fairness is the most critical review element for credit scoring systems. Anti- discrimination laws (Equal Credit Opportunity Act, Fair Housing Act) require that credit decisions be explainable and not discriminatory. Fairness testing detects whether the system produces disparate outcomes across demographic groups, while explainability ensures individual decisions can be justified if challenged.
Why A is Wrong: Access token logging is a security audit trail mechanism. While important for access governance, it does not address the primary regulatory and ethical obligations of a credit scoring system regarding decision quality and fairness.
Why C is Wrong: Stakeholder communication of performance metrics is a governance reporting activity.
Metric communication does not confirm the system is making fair, explainable decisions-it only reports on performance indicators.
Why D is Wrong: User ease of learning and use is a user experience and adoption concern. System usability does not determine whether credit scoring decisions are accurate, fair, or legally compliant-which are the primary post-implementation concerns.
NEW QUESTION # 151
A risk practitioner learns that an AI system used by a manufacturer for quality control (QC) has produced inaccurate responses that could potentially impact user safety. Which of the following is the risk practitioner's BEST recommendation to mitigate this risk?
Answer: D
Explanation:
When an AI system used for safety-critical quality control produces inaccurate responses that could harm users, the most immediate and effective safeguard is inserting human judgment into the decision process before unsafe outputs can reach production or end users.
Why A is Correct: The ISACA AAIR human oversight guidance identifies human-in-the-loop reviews as the most effective mitigation when AI outputs pose safety risks. In safety-critical applications like manufacturing quality control, human reviewers can catch and correct AI errors before they result in unsafe products reaching consumers. This control is immediately implementable, does not require model retraining, and directly addresses the safety risk. It is the appropriate response when AI accuracy cannot be fully trusted.
Why B is Wrong: Bias and fairness testing is a model evaluation activity that assesses whether outputs are systematically skewed. While useful for improving the model, it does not provide immediate protection against the safety risk of current inaccurate outputs.
Why C is Wrong: Synthetic data augmentation may improve model quality over time but requires model retraining and does not prevent currently inaccurate outputs from causing harm in the interim.
Why D is Wrong: Prompt engineering training improves how users interact with AI systems to elicit better outputs. It is useful for generative AI applications but does not directly address safety risks from QC system inaccuracies, which require operational oversight rather than improved prompting.
NEW QUESTION # 152
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