Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 211:

  During the deployment of a generative AI platform, a risk assessment highlighted threats such as data leakage and prompt manipulation. Which of the following is the BEST way to ensure appropriate control selection?

  A. Rely primarily on vendor-provided security features and seek third-party certifications
  B. Map identified AI threats to enterprise control catalogs and integrate AI-specific safeguards where gaps exist
  C. Apply AI-specific controls from external frameworks without customization and initiate monitoring to expedite compliance
  D. Postpone control selection until deployment and address risk through enhanced monitoring
  B. Map identified AI threats to enterprise control catalogs and integrate AI-specific safeguards where gaps exist

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  AAISM requires that control selection be threat-led and context-specific, aligning AI threats to the organization's existing enterprise control catalogs (security, privacy, resilience) and augmenting them with AI-specific safeguards where coverage is insufficient. This ensures consistency with the risk appetite, removes duplication, and closes AI-unique gaps (e.g., prompt injection, data leakage from context windows, model misuse). Generic reliance on vendors or uncustomized external frameworks does not ensure fit-for-purpose coverage, and deferring control selection to post-deployment contradicts proactive risk treatment.

  References: AI Security Management (AAISM) Body of Knowledge - Governance and Program Controls; Control Selection and Tailoring; Threat-to-Control Mapping for AI Systems; Risk Appetite and Control Assurance Alignment.

• Question 212:

  Which BEST addresses hallucination risk in AI systems?

  A. Human oversight Recursive
  B. chunking Automated output
  C. validation Content
  D. enrichment
  A. Human oversight Recursive

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  AAISM states that human oversight is the strongest control for hallucination risks, especially in high-impact decisions. Humans validate outputs, correct errors, and override unsafe model responses. Automated validation (C) helps but cannot fully detect hallucinations. Chunking (B) improves information handling but not hallucination mitigation. Content enrichment (D) does not reduce hallucinations.

  References: AAISM Study Guide - Human-in-the-Loop (HITL) Controls; Hallucination Mitigation.

• Question 213:

  An organization is updating its vendor arrangements to facilitate the safe adoption of AI technologies. Which of the following would be the PRIMARY challenge in delivering this initiative?

  A. Failure to adequately assess AI risk
  B. Inability to sufficiently identify shadow AI within the organization
  C. Unwillingness of large AI companies to accept updated terms
  D. Insufficient legal team experience with AI
  C. Unwillingness of large AI companies to accept updated terms

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  In the AAISM. guidance, vendor management for AI adoption highlights that large AI providers often resist contractual changes, particularly when customers seek to impose stricter security, transparency, or ethical obligations. The official study materials emphasize that while organizations must evaluate AI risk and build internal expertise, the primary challenge lies in negotiating acceptable contractual terms with dominant AI vendors who may not be willing to adjust their standardized agreements. This resistance limits the ability of organizations to enforce oversight, bias controls, and compliance requirements contractually.

  References: AAISM Exam Content Outline - AI Risk Management AI Security Management Study Guide - Third-Party and Vendor Risk

• Question 214:

  An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?

  A. Mandate an AI security audit by an external auditor before procurement
  B. Initiate discussions between the organization's and the vendor's legal teams
  C. Ensure vendors disclose how the application uses the organization's data
  D. Assess the vendor's publicly available AI usage policy
  C. Ensure vendors disclose how the application uses the organization's data

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  AAISM's approach to third-party and vendor risk for AI systems stresses data usage transparency as a primary control. The guidance explains that organizations must obtain clear documentation on "what data is collected, how it is processed, stored, retained, and whether it is reused for training or shared with other parties." Option C directly addresses this by requiring the vendor to disclose how the application uses organizational data, enabling appropriate risk assessment, contractual controls, and technical safeguards. An external audit (A) can be useful but may be costly and not always feasible pre-procurement. Legal discussions (B) are important but ineffective without clarity on data flows. Publicly available policies (D) are often high-level and marketing-oriented, lacking the specificity required for proper risk evaluation. Therefore, obtaining explicit data usage disclosures from the vendor is the most effective starting point.

  References: AI Security Management. (AAISM) Study Guide - Third-Party AI Risk and Data Sharing; Vendor Governance Requirements.

• Question 215:

  A healthcare provider deploys an AI system to predict patient deterioration. Which factor is MOST important to ensure ethical deployment?

  A. Response latency under peak load
  B. Explainability of predictions
  C. GPU utilization efficiency
  D. Hardware redundancy
  B. Explainability of predictions

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  AAISM stresses high explainability for safety-critical and human-impacting decisions such as healthcare predictions. Technical performance (A, C, D) does not outweigh transparency requirements.

  References:

  AAISM Study Guide ?Explainability for High-Impact Use Cases.

• Question 216:

  When evaluating a new AI tool for intrusion prevention, which is MOST important to ensure fit within the existing program architecture?

  A. Ensure automated response orchestration
  B. Prioritize real-time anomaly detection
  C. Confirm tool capabilities align with control objectives
  D. Select a tool that integrates with the SIEM
  C. Confirm tool capabilities align with control objectives

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  AAISM stresses that AI tools must align with the organization's existing control objectives and governance requirements, ensuring consistency with risk management, detection philosophy, and operational processes. Integration with SIEM (D) is important but secondary. Anomaly detection (B) is a feature, not an architectural requirement. Automated orchestration (A) is optional.

  References: AAISM Study Guide - AI Security Architecture and Control Alignment.

• Question 217:

  Which of the following BEST describes an adversarial attack on an AI model?

  A. Attacking the underlying hardware of the AI system
  B. Providing inputs that mislead the AI model into incorrect predictions
  C. Reverse engineering the AI model using social engineering techniques
  D. Conducting denial-of-service (DoS) attacks against AI APIs
  B. Providing inputs that mislead the AI model into incorrect predictions

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  In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se. Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation. References:AI Security Management (AAISM) Body of Knowledge: Taxonomy of AI Threats-Adversarial Examples and Evasion; Distinction from System/Network Attacks.AAISM Study Guide: Definitions and Threat Models in Adversarial ML; Attack-Defense Mapping for Model Predictions.

• Question 218:

  Which of the following AI-driven systems should have the MOST stringent recovery time objective (RTO)?

  A. Health support system
  B. Credit risk modeling system
  C. Car navigation system
  D. Industrial control system
  D. Industrial control system

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  AAISM risk guidance notes that the most stringent recovery objectives apply to industrial control systems, as downtime can directly disrupt critical infrastructure, manufacturing, or safety operations. Health support systems also require high availability, but industrial control often underpins safety-critical and real-time environments where delays can result in catastrophic outcomes. Credit risk models and navigation systems are important but less critical in terms of immediate physical and operational impact.

  Thus, industrial control systems require the tightest RTO.

  References:

  AAISM Study Guide - AI Risk Management (Business Continuity in AI)

  ISACA AI Security Management - RTO Priorities for AI Systems

• Question 219:

  Which of the following is the MOST effective way to mitigate the risk of deepfake attacks?

  A. Relying on human judgment for oversight
  B. Limiting employee access to AI tools
  C. Validating the provenance of the data source
  D. Using a general-purpose large language model (LLM) to detect fraud
  C. Validating the provenance of the data source

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  AAISM study content identifies validating the provenance of data sources as the most effective way to counter deepfake risks. Provenance validation ensures that content is authentic, verifiable, and traceable, preventing malicious synthetic media from being trusted as legitimate. Human oversight helps but cannot reliably detect sophisticated fakes. Limiting tool access reduces exposure but does not prevent external attacks. General-purpose LLMs are not optimized for fraud detection. The strongest control

  is verifying the origin and authenticity of data before acceptance.

  References:

  AAISM Study Guide - AI Risk Management (Deepfake and Content Integrity Risks)

  ISACA AI Security Management - Provenance Validation as a Defense

• Question 220:

  Which activity BEST prepares an organization to respond to emerging AI threats?

  A. Annual certification of AI developers
  B. Quarterly model architecture reviews
  C. Continuous adversarial threat-intelligence monitoring
  D. Rotating training datasets more frequently
  C. Continuous adversarial threat-intelligence monitoring

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  AAISM recommends continuous monitoring of adversarial ML tactics, emerging vulnerabilities, and attack research to proactively adapt controls. Architecture reviews (B) are important but not threat-focused. Annual certifications (A) lack immediacy. Dataset rotation (D) does not guarantee preparedness.

  References:

  AAISM Study Guide ?Threat Intelligence for AIand; Adversarial ML.