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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - AI risk assessment and treatment strategies - AI risk monitoring and continuous improvement - AI governance communication and reporting - Enterprise AI risk program design |
| AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability |
| AI Life Cycle Risk Management | - AI model and data risk identification - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation |
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NEW QUESTION # 84
Which of the following is the MOST important reason for a risk practitioner to classify AI risk using threat actor profiles?
Answer: D
Explanation:
Threat actor profiling characterizes the motivations, capabilities, and likely attack methods of potential adversaries. In AI risk management, understanding who the likely attackers are and what they seek enables the design of controls specifically matched to the actual threat landscape.
Why B is Correct: According to ISACA AAIR threat-based risk management guidance, the most important reason for threat actor profiling is to tailor controls to adversary motivations and capabilities. Different threat actors-nation-state attackers, criminal organizations, competitors, insiders, activists-have different objectives (espionage vs. financial gain vs. disruption), capabilities (sophisticated vs. opportunistic), and methods. Controls calibrated to actual threat actor profiles are significantly more effective than generic controls that may not address the specific threats the organization actually faces.
Why A is Wrong: Aligning AI threats with IT control taxonomy is a governance integration activity that improves control consistency but does not capture the threat actor-specific tailoring value of profiling.
Taxonomy alignment is an administrative benefit; threat-tailored controls are a security effectiveness benefit.
Why C is Wrong: Response metrics for cybersecurity incidents are developed for incident management planning. Threat actor profiling informs control design and incident response strategies but is not primarily used to develop response metrics.
Why D is Wrong: Prioritizing external threats over internal threats is a security strategy choice that threat actor profiling does not prescribe. Many AI attacks, including insider threats and social engineering, are internal. Profiling should result in appropriate prioritization based on actual threat likelihood, not a blanket prioritization of external threats.
NEW QUESTION # 85
A risk practitioner is concerned that an AI model's responses have become more inaccurate over time, leading to diminished customer trust. Which of the following should the risk practitioner recommend be done FIRST?
Answer: D
Explanation:
Incident response for AI model degradation should follow a structured diagnostic process. Before implementing any corrective action, the scope and nature of the accuracy issues must be understood to ensure the response is appropriate and targeted.
Why D is Correct: According to ISACA AAIR incident response guidance, the first step when AI model accuracy deteriorates is to assess the impact-understanding which specific features are affected, how model outputs have changed, and what the business consequences are. This diagnostic step informs all subsequent decisions about whether to retrain, add validation cycles, or take the system offline. Acting without this assessment may waste resources on inappropriate responses or leave critical issues unaddressed.
Why A is Wrong: Adding validation review cycles is a process change that may be appropriate but cannot be determined without first understanding the nature and scope of the accuracy problem. Reviews address a symptom without diagnosing the cause.
Why B is Wrong: Taking the model offline and backing it up is a drastic operational measure that may be disproportionate to the actual issue. This decision requires understanding the severity and scope of the problem, which requires impact assessment first.
Why C is Wrong: Full model retraining is resource-intensive and may not address the root cause if the problem is not training data staleness. Impact assessment must precede the decision to retrain.
NEW QUESTION # 86
An organization integrates multiple AI services using APIs to enhance a customer support chatbot. Which of the following is the GREATEST risk?
Answer: A
Explanation:
API integration with external AI services creates data transmission pathways between the organization and external systems. Customer support contexts involve sensitive personal data-account information, contact details, inquiry content-that may be transmitted through these API connections.
Why B is Correct: The ISACA AAIR security and privacy guidance identifies unauthorized disclosure of sensitive data through insecure API connections as the greatest risk in multi-service AI integration. APIs can be vulnerable to interception, inadequate authentication, or misconfiguration. In a customer support context, exposure of personal data via API vulnerabilities creates privacy violations, regulatory liability, and reputational harm-all more severe than the other listed concerns.
Why A is Wrong: Bias and inaccuracy in chatbot responses are real quality risks but represent service quality issues rather than security or privacy breaches. Inaccurate responses are visible and correctable; data breaches may go undetected.
Why C is Wrong: Customer dissatisfaction from operational delays is a service quality and business risk. It is a manageable consequence of performance issues rather than the greatest risk from API-based AI integration.
Why D is Wrong: Insufficient training datasets affect model quality but are a development concern addressed during the model selection phase. They do not represent the primary operational risk of deploying multi- service API integrations in production.
NEW QUESTION # 87
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 # 88
An organization uses AI to generate procedure documents for operational processes. Which of the following would be of GREATEST concern to a risk practitioner?
Answer: D
Explanation:
AI-generated content-including operational procedures-can contain errors, omissions, hallucinations, and contextually inappropriate guidance. Human review is a critical quality control and accountability mechanism that ensures generated procedures are accurate, complete, and appropriate for actual operational use.
Why A is Correct: The ISACA AAIR guidance on human oversight identifies the absence of human review as the greatest risk in AI-generated documentation. Without review, errors and AI hallucinations are propagated directly into operational use, potentially causing safety incidents, compliance violations, or operational failures. Human review is the last line of defense against AI output quality failures, particularly in operational procedure contexts where incorrect instructions can have serious consequences.
Why B is Wrong: Outdated procedures are a content quality issue that would typically be caught during human review. The greater concern is that no review is occurring, which allows all types of errors-including outdated content-to reach operational use unchallenged.
Why C is Wrong: Policy misalignment is a governance concern but represents a specific type of error that would be identified if adequate human review were performed. The absence of review is the root governance failure.
Why D is Wrong: Using AI to generate procedures for high-risk activities is a deployment scope concern that raises the stakes of errors. However, the fundamental governance failure-and the greatest concern-is that no human verification occurs regardless of the risk level of the activity.
NEW QUESTION # 89
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