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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q86-Q91):

NEW QUESTION # 86
Which of the following is the MOST important consideration for an organization that has decided to adopt AI to leverage its competitive advantage?

A. Develop a comprehensive risk management process to address AI-related issues
B. Develop a comprehensive strategic roadmap for AI integration
C. Develop internal training programs on AI governance, risk, and compliance (GRC)
D. Develop a business case for the procurement of AI monitoring tools

Answer: B

Explanation:
AAISM's governance guidance emphasizes that adopting AI for competitive advantage must begin with a comprehensive strategic roadmap for integration. This roadmap aligns AI adoption with business objectives, sets priorities, defines milestones, and ensures coordination across functions. Risk management, training, and tool procurement are essential, but they are tactical steps that follow once the strategic direction is defined.
Without a roadmap, adoption becomes fragmented and risks misalignment with business strategy. The most important consideration at the adoption stage is therefore creating a strategic integration roadmap.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Strategy and Roadmapping) AI Security Management Study Guide - Business Alignment of AI Initiatives

NEW QUESTION # 87
Which of the following is a key risk indicator (KRI) for an AI system used for threat detection?

A. Number of system overrides by cyber analysts
B. Training time of the model
C. Number of training epochs
D. Number of layers in the neural network

Answer: A

Explanation:
AAISM materials emphasize that in operational AI systems, key risk indicators (KRIs) must reflect risks to performance and reliability rather than technical design factors alone. In the case of threat detection, the most relevant KRI is the frequency of system overrides by human analysts, as this indicates a lack of trust, frequent false positives, or poor detection accuracy. Training epochs, model depth, and training time are technical metrics but do not directly measure operational risk. Analyst overrides represent a practical measure of system effectiveness and risk.
References:
AAISM Study Guide - AI Risk Management (Operational KRIs for AI Systems) ISACA AI Security Management - Monitoring AI Effectiveness

NEW QUESTION # 88
An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data.
Which of the following is the MOST effective data cleaning technique to improve model performance?

A. Applying statistical methods to address missing data and reduce bias
B. Deleting outlier data points to prevent unusual values impacting the model
C. Tuning model hyperparameters to increase performance and accuracy
D. Increasing the frequency of model retraining with the existing data set

Answer: A

Explanation:
The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction.
Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.
References:
AAISM Study Guide - AI Risk Management (Data Integrity and Quality Controls) ISACA AI Governance Guidance - Data Preparation and Bias Mitigation

NEW QUESTION # 89
An organization plans to implement a new AI system. Which of the following is the MOST important factor in determining the level of risk monitoring activities required?

A. The organization's risk tolerance
B. The organization's compensating controls
C. The organization's risk appetite
D. The organization's number of AI system users

Answer: A

Explanation:
AAISM risk management guidance clarifies that the organization's risk tolerance is the most important factor in determining how much monitoring is needed. Risk tolerance specifies the amount of risk the organization is willing to accept and defines the threshold for triggering monitoring or mitigation activities. Risk appetite is broader and strategic, while tolerance sets the operational limits. The number of users may influence scale, and compensating controls may affect resilience, but neither dictates monitoring intensity as directly as risk tolerance.
References:
AAISM Study Guide - AI Risk Management (Risk Appetite vs. Tolerance)
ISACA AI Security Management - Monitoring Based on Risk Tolerance

NEW QUESTION # 90
A large language model (LLM) has been manipulated to provide advice that serves an attacker's objectives.
Which of the following attack types does this situation represent?

A. Data poisoning
B. Model inversion
C. Evasion attack
D. Privilege escalation

Answer: C

Explanation:
AAISM categorizes the manipulation of an LLM at inference time, where crafted inputs cause outputs to serve attacker objectives, as an evasion attack. Evasion attacks exploit weaknesses in the model's decision- making boundaries by altering queries to produce compromised or misleading outputs. Privilege escalation refers to unauthorized access rights, data poisoning targets the training phase, and model inversion reconstructs training data. In this case, manipulation of outputs to align with an attacker's goals reflects an evasion attack.
References:
AAISM Exam Content Outline - AI Risk Management (Adversarial Attack Types) AI Security Management Study Guide - Evasion and Manipulation Risks

NEW QUESTION # 91
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AAISM Latest Test Labs - Exam AAISM Demo

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q86-Q91):

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