Latest Study AAISM Questions | Latest AAISM Study Materials
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P.S. Free 2026 ISACA AAISM dumps are available on Google Drive shared by Exam4Free: https://drive.google.com/open?id=1HbO2vML1atDd8xW-HQuaACbTuqjWYB6W
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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q134-Q139):
NEW QUESTION # 134
Which of the following is the MOST important consideration when deciding how to compose an AI red team?
- A. AI use cases
- B. Resource availability
- C. Time-to-market constraints
- D. Compliance requirements
Answer: A
Explanation:
AAISM materials specify that the composition of an AI red team must be tailored to the organization's AI use cases. The purpose of red-teaming is to simulate realistic adversarial conditions aligned with the actual applications of AI. For example, testing a generative model requires different expertise than testing a fraud detection system. While resource availability, compliance requirements, and time-to-market pressures are practical considerations, they are secondary to aligning team expertise with use case scenarios. The most important factor is therefore the AI use cases themselves.
References:
AAISM Exam Content Outline - AI Risk Management (Red Teaming Considerations) AI Security Management Study Guide - Tailoring Adversarial Testing to Use Cases
NEW QUESTION # 135
A large financial institution is integrating a third-party AI solution into its fraud detection system. Which is the BEST way to reduce AI vendor/supply chain risk?
- A. Focus on performance testing
- B. Conduct annual vulnerability assessments after integration
- C. Establish contractual agreements requiring evidence of secure development practices
- D. Use isolated virtual environments to validate integration
Answer: C
Explanation:
AAISM emphasizes contractual governance controls as the strongest mechanism for managing AI vendor and supply chain dependencies. Contracts must require:
* secure development lifecycle practices
* documentation of controls
* security testing
* supply chain transparency
Isolated environments (C) help, but they do not ensure upstream integrity. Performance testing (D) is unrelated to security. Annual assessments (A) occur too late to mitigate onboarding risks.
References: AAISM Study Guide - AI Vendor Governance; Contractual Risk Controls.
NEW QUESTION # 136
Which of the following MOST effectively addresses bias in generative AI models?
- A. Adversarial training
- B. Fairness constraints
- C. Data augmentation
- D. Data minimization
Answer: B
Explanation:
AAISM identifies fairness constraints as a direct mechanism to mitigate and control model bias by embedding fairness requirements into optimization objectives during training.
Data augmentation (B) helps but is not a primary anti-bias control. Adversarial training (C) focuses on robustness, not fairness. Minimization (A) reduces data, often making bias worse.
References: AAISM Study Guide - Fairness, Bias Mitigation Techniques, Ethical AI Controls.
NEW QUESTION # 137
Which of the following would MOST effectively ensure an organization developing AI systems has comprehensive data classification and inventory management?
- A. Implementing an automated data cataloging tool that integrates with all organizational data repositories
- B. Establishing a manual process to categorize data based on business needs and regulatory compliance
- C. Creating a centralized team to oversee the classification of data used in AI projects
- D. Conducting quarterly audits of AI data sets for anomalies and missing metadata
Answer: A
Explanation:
AAISM governance practices emphasize automation as the most effective way to maintain comprehensive, accurate, and scalable data classification and inventory management. An automated data cataloging tool integrated with all repositories ensures continuous visibility, reduces human error, and supports regulatory compliance. Centralized teams and manual processes provide oversight but cannot achieve the same scale and consistency. Periodic audits detect issues but are reactive rather than proactive. For effective governance, the best solution is automated cataloging integrated across data repositories.
References:
AAISM Study Guide - AI Governance and Program Management (Data Classification and Inventory) ISACA AI Security Management - Automated Data Cataloging for AI Projects
NEW QUESTION # 138
A CISO must provide KPIs for the organization's newly deployed AI chatbot. Which metrics are BEST?
- A. Explainability and F1 score
- B. Customer effort score and user retention
- C. Response time and throughput
- D. Error rate and bias detection
Answer: D
Explanation:
AAISM recommends that AI KPIs should emphasize:
* Error rates - measure correctness and reliability
* Bias detection metrics - assess fairness and harm risk
These are the core governance KPIs for AI systems interacting with end users.
Response time (A) is a performance metric but not an AI governance KPI. Customer retention (C) is business- focused. F1 score (D) is useful but not as critical as bias monitoring.
References: AAISM Study Guide - AI Performance, Fairness & Governance Metrics.
NEW QUESTION # 139
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