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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which of the following controls BEST mitigates the risk of bias in AI models?

• A. Cryptographic hash functions
• B. Regular data reconciliation
• C. Diverse data sourcing strategies
• D. Robust access control techniques

Answer: C

Explanation:
Bias in AI models primarily stems from limitations or imbalances in training data. The AAISM study materials emphasize that the most effective way to mitigate this risk is through diverse data sourcing strategies that ensure coverage across demographics, scenarios, and contexts. Access controls protect data security, not fairness. Data reconciliation ensures accuracy but does not address representational imbalance.
Cryptographic hashing preserves integrity but has no impact on bias mitigation. To reduce systemic unfairness, the critical control is sourcing diverse and representative data.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Bias and Fairness Management) AI Security Management Study Guide - Data Governance and Bias Reduction Strategies

 

NEW QUESTION # 84
An organization is reviewing an AI application to determine whether it is still needed. Engineers have been asked to analyze the number of incorrect predictions against the total number of predictions made. Which of the following is this an example of?

• A. Key performance indicator (KPI)
• B. Control self-assessment (CSA)
• C. Explainable decision-making
• D. Model validation

Answer: A

Explanation:
AAISM guidance identifies metrics like error rate versus total predictions as a key performance indicator (KPI) for evaluating AI model effectiveness. KPIs provide measurable values to assess performance against objectives. Model validation is broader and occurs prior to production use, testing the model against predefined standards. Control self-assessment relates to governance processes, not predictive accuracy.
Explainable decision-making refers to interpretability, not error-rate evaluation. Thus, analyzing incorrect predictions against total predictions is a performance measure, making it a KPI.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Performance Metrics and KPIs) AI Security Management Study Guide - Accuracy and Error Metrics

 

NEW QUESTION # 85
Which of the following information is MOST important to include in a centralized AI inventory?

• A. AI model use cases
• B. Foundation model and package registry
• C. Ownership and accountability of AI systems
• D. Training data sets

Answer: C

Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.
References:
AAISM Exam Content Outline - AI Governance and Program Management (AI Inventories and Oversight) AI Security Management Study Guide - Ownership and Accountability Structures

 

NEW QUESTION # 86
An organization utilizes AI-enabled mapping software to plan routes for delivery drivers. A driver following the AI route drives the wrong way down a one-way street, despite numerous signs. Which of the following biases does this scenario demonstrate?

• A. Automation
• B. Reporting
• C. Confirmation
• D. Selection

Answer: A

Explanation:
AAISM defines automation bias as the tendency of individuals to over-rely on AI-generated outputs even when contradictory real-world evidence is available. In this scenario, the driver ignores traffic signs and follows the AI's instructions, showing blind reliance on automation. Selection bias relates to data sampling, reporting bias refers to misrepresentation of results, and confirmation bias involves interpreting information to fit pre-existing beliefs. The most accurate description is automation bias.
References:
AAISM Exam Content Outline - AI Risk Management (Bias Types in AI)
AI Security Management Study Guide - Automation Bias in AI Use

 

NEW QUESTION # 87
Which of the following is the BEST mitigation control for membership inference attacks on AI systems?

• A. Cybersecurity-oriented red teaming
• B. AI threat modeling
• C. Model ensemble techniques
• D. Differential privacy

Answer: D

Explanation:
Membership inference attacks attempt to determine whether a particular data point was part of a model's training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Techniques) ISACA AI Security Management - Membership Inference Mitigations

 

NEW QUESTION # 88
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