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Make Exam Preparation Simple ISACA AAISM Exam Questions

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Make Exam Preparation Simple ISACA AAISM Exam Questions

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q88-Q93):NEW QUESTION # 88
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. Explainable decision-making
  • C. Control self-assessment (CSA)
  • 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 # 89
An organization is deploying an automated AI cybersecurity system. Which strategy MOST effectively minimizes human error and improves security?
  • A. Utilizing machine learning algorithms to ensure responsible use
  • B. Using historical data to train detection software
  • C. Conducting periodic penetration testing
  • D. Manual monitoring of alerts
Answer: B
Explanation:
AAISM states that the effectiveness of automated AI cybersecurity systems depends heavily on well-trained detection models using high-quality historical attack data.
Historical data improves:
* detection accuracy
* reduction of false positives
* reduction of human misinterpretation
Manual monitoring (A) increases human error. ML "ensuring responsibility" (C) is not a defined control. Pen testing (D) does not reduce human mistakes.
References: AAISM Study Guide - AI in Cybersecurity; Model Training for Threat Detection.

NEW QUESTION # 90
Which of the following is MOST important to consider when validating a third-party AI tool?
  • A. Right to audit
  • B. Terms and conditions
  • C. Industry analysis and certifications
  • D. Roundtable testing
Answer: A
Explanation:
The AAISM framework specifies that when adopting third-party AI tools, the right to audit is the most critical contractual and governance safeguard. This ensures that the organization can independently verify compliance with security, privacy, and ethical requirements throughout the lifecycle of the tool. Terms and conditions provide general usage guidance but often limit liability rather than ensuring transparency. Industry certifications may indicate good practice but do not substitute for direct verification. Roundtable testing is useful for evaluation but lacks enforceability. Only the contractual right to audit provides formal assurance that the tool operates in accordance with organizational policies and external regulations.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Third-Party Governance) AI Security Management Study Guide - Vendor Oversight and Audit Rights

NEW QUESTION # 91
Which of the following is MOST important for effective AI risk management?
  • A. Utilization of best practice AI risk management frameworks
  • B. Internal stakeholder participation in AI risk management processes
  • C. Creation of separate risk management processes for AI-specific risk
  • D. Risk measurement during an early stage of the AI system life cycle
Answer: D
Explanation:
AAISM positions early and continuous risk assessment as a critical success factor. The guidance states that AI risk should be "identified, analyzed, and measured starting from the design and concept phases, before significant investment and deployment." This ensures that high-impact risks (e.g., bias, privacy violations, safety issues) can be mitigated or designed-out before they become embedded in production systems.
Frameworks (A) are valuable, but their effectiveness depends on when and how they are applied. Stakeholder participation (B) is important but is one component of a broader process. Creating completely separate risk processes for AI (D) may fragment governance and is not required; integration with enterprise risk management is preferred. Thus, the timing of risk measurement-early in the life cycle-is identified as the most important factor for effective AI risk management.
References: AI Security Management™ (AAISM) Study Guide - AI Risk Management Life Cycle; Design- Time Risk Identification.

NEW QUESTION # 92
Which of the following AI system vulnerabilities is MOST easily exploited by adversaries?
  • A. Weak controls for access to the AI model
  • B. Inability to detect input modifications causing inappropriate AI outputs
  • C. Lack of protection against denial of service (DoS) attacks
  • D. Inaccurate generalizations from new data by the AI model
Answer: A
Explanation:
AAISM study materials stress that weak access controls are the most easily exploited vulnerability in AI systems. Without strong access restrictions, adversaries can directly query, extract, manipulate, or overload models, leading to data leakage or compromised outputs. While inaccurate generalizations, DoS vulnerabilities, or susceptibility to input manipulation are serious, they typically require more effort or specific conditions. Weak access control provides the most direct and immediate entry point for attackers. As such, it is identified as the most easily exploited vulnerability.
References:
AAISM Exam Content Outline - AI Risk Management (Access and Authentication Vulnerabilities) AI Security Management Study Guide - Exploitable Weaknesses in AI Systems

NEW QUESTION # 93
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