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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q194-Q199):

NEW QUESTION # 194
Which of the following is the MOST effective way to identify and address security risk in an AI model?

Answer: D

Explanation:
AI/ML threat modeling is the most effective structured method to both identify and address model security risks. It systematically surfaces attack classes (poisoning, evasion, membership inference, model extraction, inversion), maps system-specific attack surfaces (data pipelines, feature stores, training artifacts, inference APIs), and drives prioritized mitigations (ingestion validation, robust training, rate-limiting, watermarking, differential privacy, monitoring, red teaming). Output spot-checking (A) finds errors but not security vulnerabilities; encryption (C) protects confidentiality but does not reveal threats or mitigate inference-time attacks; adding data (D) may improve accuracy but does not target adversarial risk.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Identification & Threat Modeling; Attack Surface Analysis for ML; Risk Treatment Planning. AAISM Study Guide - Evasion
/Poisoning/Extraction Controls; Mapping Risks to Controls; Validation and Assurance Activities.


NEW QUESTION # 195
Personal data used to train AI systems can BEST be protected by:

Answer: B

Explanation:
AAISM guidance on privacy-preserving AI highlights anonymization as the most effective means of protecting personal data used in training. By irreversibly removing or masking identifiable attributes, anonymization ensures that training data cannot be linked back to individuals, thereby meeting key privacy obligations under laws such as GDPR. Erasing data after training may limit exposure but does not protect it during the training process. Ensuring data quality improves accuracy but does not mitigate privacy risk.
Hashing protects data integrity but does not guarantee anonymity, as hashes can sometimes be reversed or correlated. Therefore, anonymization is the recommended control for protecting personal data in AI training.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Methods) ISACA AI Security Management - Data Anonymization Practices


NEW QUESTION # 196
Which of the following MOST effectively secures ongoing stakeholder support for AI initiatives?

Answer: C

Explanation:
AAISM governance guidance emphasizes that stakeholder buy-in is sustained when the measurable value of AI initiatives is clearly communicated. Value demonstrations include:
* improved efficiency
* reduced cost
* reduced risk
* business growth
Training (B) and risk optimization (C) are important but do not guarantee stakeholder support. A roadmap (D) guides planning but does not secure buy-in.
References: AAISM Study Guide - AI Governance; Stakeholder Engagement & Value Communication.


NEW QUESTION # 197
Which of the following key risk indicators (KRIs) is MOST relevant when evaluating the effectiveness of an organization's AI risk management program?

Answer: B

Explanation:
AAISM identifies percentage of AI projects in compliance as the most relevant KRI for evaluating AI risk management effectiveness. This metric directly reflects adherence to governance, regulatory, and security requirements. The number of models deployed (A) or systems with AI components (B) indicate scale, not risk management quality. Training requests (D) show awareness levels but do not measure effectiveness of risk management. Compliance percentage provides a direct, measurable indication of how well risks are being governed and mitigated.
References:
AAISM Exam Content Outline - AI Risk Management (Risk Metrics and Compliance) AI Security Management Study Guide - Key Risk Indicators in AI Programs


NEW QUESTION # 198
Which of the following AI-driven systems should have the MOST stringent recovery time objective (RTO)?

Answer: C

Explanation:
AAISM risk guidance notes that the most stringent recovery objectives apply to industrial control systems, as downtime can directly disrupt critical infrastructure, manufacturing, or safety operations. Health support systems also require high availability, but industrial control often underpins safety-critical and real-time environments where delays can result in catastrophic outcomes. Credit risk models and navigation systems are important but less critical in terms of immediate physical and operational impact. Thus, industrial control systems require the tightest RTO.
References:
AAISM Study Guide - AI Risk Management (Business Continuity in AI)
ISACA AI Security Management - RTO Priorities for AI Systems


NEW QUESTION # 199
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