Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 91:

  Which of the following is MOST important to monitor in order to ensure the effectiveness of an organization' s AI vendor management program?

  A. Vendor compliance with AI-related requirements
  B. Vendor reviews of external AI threat reports
  C. Vendor results in compliance training programs
  D. Vendor participation in industry AI research
  A. Vendor compliance with AI-related requirements

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  The AAISM framework specifies that the primary metric of effectiveness in vendor management is the vendor's compliance with AI-related requirements defined in contracts and governance frameworks. This provides measurable assurance that vendors adhere to agreed-upon privacy, security, and ethical standards. Reviews of threat reports, training results, or research participation are supplemental and may support continuous improvement, but they do not establish compliance accountability. Governance

  requires a direct focus on whether contractual and regulatory obligations are being fulfilled. Therefore, vendor compliance with AI requirements is the most important monitoring focus.

  References:

  AAISM Study Guide - AI Risk Management (Third-Party Risk Oversight)

  ISACA AI Security Management - Vendor Compliance Monitoring

• Question 92:

  A company integrates an autonomous AI agent to execute scheduled operational tasks. What is the GREATEST risk?

  A. Low training accuracy
  B. Over-reliance on default hyperparameters
  C. Unauthorized decision-making due to excessive agency
  D. Slow inference speed
  C. Unauthorized decision-making due to excessive agency

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  AAISM identifies "excessive agency" as a major risk for autonomous agents, where systems execute actions beyond intended scope, potentially causing security or compliance failures. Hyperparameters (B) and accuracy (A) are secondary. Speed (D) is not a major risk.

  References:

  AAISM Study Guide ?Autonomous Agentsand; Excessive Agency Controls.

• Question 93:

  A CISO has been tasked with providing key performance indicators (KPIs) on the organization's newly launched AI chatbot. Which of the following are the BEST metrics for the CISO to recommend?

  A. Explainability and F1 score
  B. Customer effort score and user retention rate
  C. Response time and throughput
  D. Error rate and bias detection
  D. Error rate and bias detection

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  For executive security and governance reporting, AAISM prioritizes risk- and harm-oriented KPIs that reflect safety, reliability, and responsible behavior of AI systems. Error rate (safety/quality signal) and bias detection (fairness/compliance signal) provide leading indicators of model risk, potential user harm, and regulatory exposure-key interests for a CISO. Explainability and F1 (A) are model performance/interpretability metrics; customer effort/retention (B) are business CX metrics; response time/throughput

  (C) are operational SRE metrics. While valuable, they are secondary to risk-centric KPIs for CISO oversight.

  References: AI Security Management (AAISM) Body of Knowledge - AI Risk Metrics and Assurance; Governance Dashboards for AI. AAISM Study Guide - Operationalizing AI Controls; Safety, Fairness, and Compliance Indicators for Executive Reporting. O Error rate and bias detection

• Question 94:

  For a life insurance company deploying AI for fraud detection, which factor is MOST critical?

  A. Robustness
  B. Accuracy
  C. Explainability
  D. Adaptability
  A. Robustness

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  AAISM emphasizes robustness as the key requirement for fraud-detection systems because they must resist adversarial manipulation, data poisoning, spoofing, and input tampering. Accuracy (B) matters but does not protect against adversarial attacks. Explainability (C) is important but secondary. Adaptability (D) is useful but not the top security requirement.

  References: AAISM Study Guide - AI Robustness; Fraud Detection and Adversarial Resistance.

• Question 95:

  When an attacker uses synthetic data to reverse engineer an organization's AI model, it is an example of which of the following types of attack?

  A. Distillation
  B. Inversion
  C. Prompt
  D. Poisoning
  B. Inversion

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  AAISM defines model inversion attacks as those where adversaries use queries or synthetic data to reconstruct sensitive information or approximate the inner workings of a model. By exploiting outputs, attackers attempt to reverse engineer training data or model functionality. Distillation refers to compressing models, not adversarial attacks. Prompt attacks relate to manipulating language model inputs, and poisoning occurs when adversaries corrupt training data rather than infer from outputs. The scenario

  describes attackers using synthetic data to reveal hidden characteristics, which aligns directly with inversion attacks.

  References:

  AAISM Exam Content Outline - AI Technologies and Controls (Attack Types and Mitigations)

  AI Security Management Study Guide - Model Inversion Risks

• Question 96:

  The PRIMARY reason to conduct a privacy impact assessment (PIA) on an AI system is to:

  A. Identify applicable regulations
  B. Determine whether personal data is poisoned
  C. Build customer confidence
  D. Analyze how personal data is handled
  D. Analyze how personal data is handled

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  According to AAISM privacy governance guidance, the primary reason for conducting a PIA is to analyze how personal data is collected, processed, shared, and retained by an AI system. This analysis ensures compliance with privacy laws, mitigates risks to individuals, and informs necessary safeguards. Identifying regulations is part of compliance but is secondary to analyzing actual data handling. Building customer confidence is an outcome, not the main purpose. Checking for poisoned data relates to

  data quality, not privacy assessment. The fundamental purpose of a PIA is therefore to analyze the handling of personal data.

  References:

  AAISM Study Guide - AI Governance and Program Management (Privacy Impact Assessments)

  ISACA AI Security Management - Data Handling and Privacy Risk

• Question 97:

  An AI system that supports critical processes has deviated from expected performance and is producing biased outcomes. Which of the following is the BEST course of action?

  A. Retrain the model with a new and expanded dataset
  B. Perform a root cause analysis to identify mitigation steps
  C. Conduct audits of the data and the model
  D. Activate the model kill switch
  B. Perform a root cause analysis to identify mitigation steps

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  AAISM directs that when harmful or biased behavior is observed in a production AI system, the organization should enter a formal incident/variance handling workflow that begins with root cause analysis (RCA) to identify the source of deviation (data drift, concept drift, feature leakage, pipeline changes, control failures) and determine proportionate risk treatments. Immediate retraining (Option A) without RCA risks reinforcing the same bias; audits (Option C) are key activities within RCA rather than the action that frames the response; a kill switch (Option D) is reserved for conditions where risk exceeds the defined tolerances and immediate harm prevention is required.

  References: AI Security Management. (AAISM) Body of Knowledge - Incident Response and Post-Incident Improvement; Model Risk Treatment and Drift Management; Bias Detection and Remediation Governance.

• Question 98:

  Which of the following is the MOST critical success factor for an AI implementation project?

  A. Developing and using model cards
  B. Ensuring AI risk is captured in the risk register
  C. Mapping data throughout the life cycle
  D. Obtaining senior management buy-in
  D. Obtaining senior management buy-in

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  AAISM identifies executive sponsorship and senior management buy-in as the foremost success factor for AI initiatives. It secures resources, resolves cross-functional conflicts, sets risk appetite, and enforces adherence to governance and controls. Model cards (A), risk registers (B), and lifecycle data mapping (C) are vital practices within the program, but without top-level commitment, adoption, funding, and accountability often fail.

  References: AI Security Management (AAISM) Body of Knowledge - AI Program Governance; Executive Sponsorship and Accountability; Strategy-to-Control Alignment for Successful AI Delivery.

• Question 99:

  What is the GREATEST benefit of performing AI security risk assessments?

  A. Updating the risk register
  B. Implementing privacy controls
  C. Enabling risk prioritization
  D. Securing appropriate funding
  C. Enabling risk prioritization

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  AAISM emphasizes that the primary value of AI security risk assessments is prioritizing risks based on likelihood, impact, and business relevance. Updating the register (A) is administrative. Privacy controls (B) are one category of mitigation. Funding (D) is possible but not the primary purpose.

  References: AAISM Study Guide - AI Risk Assessment Objectives; Prioritization and Governance.

• Question 100:

  An organization implementing an LLM application sees unexpected cost increases due to excessive computational resource usage. Which vulnerability is MOST likely in need of mitigation?

  A. Excessive agency
  B. Sensitive information disclosure
  C. Unbounded consumption
  D. System prompt leakage
  C. Unbounded consumption

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  AAISM categorizes unbounded consumption (also known as "resource exhaustion" or "infinite queries") as an AI-specific vulnerability where attackers (or faulty prompts) trigger excessive computation, leading to high costs and degraded service.

  This aligns precisely with unexpected large compute bills.

  Excessive agency (A) refers to unsafe autonomy, while disclosure (B) and prompt leakage (D) do not relate to compute overuse.

  References: AAISM Study Guide - AI Abuse and Unbounded Consumption Risk.