Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 271:

  Which of the following methods provides the MOST effective protection against model inversion attacks?

  A. Using adversarial training
  B. Reducing the model's complexity
  C. Implementing regularization output
  D. Increasing the number of training iterations
  C. Implementing regularization output

  ---

  AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in

  outputs.

  A (adversarial training) targets perturbation robustness, not primary for inversion.

  B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.

  D (more iterations) typically increases overfitting and leakage risk.

  AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.

  References:. AI Security Management (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.. AI Security Management Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.

• Question 272:

  How can an organization BEST protect itself from payment diversions caused by deepfake attacks impersonating management?

  A. Require mandatory deepfake detection training for all employees
  B. Mandate that payments be sent only once per week
  C. Issue a security policy on deepfakes
  D. Implement resilient payment approval processes
  D. Implement resilient payment approval processes

  ---

  AAISM's risk management framework stresses that the most effective defense against deepfake-enabled fraud, such as payment diversion, is resilient payment approval processes. This includes multi-step verification, segregation of duties, and independent confirmations for high-value transactions. Employee training, policies, or limiting payment frequency may reduce exposure, but they cannot guarantee prevention. Only process-based controls enforce structural safeguards that prevent fraudulent instructions from being executed, even if a deepfake impersonation attempt is successful.

  References: AAISM Exam Content Outline - AI Risk Management (Fraud and Deepfake Risk) AI Security Management Study Guide - Transactional Resilience and Controls

• Question 273:

  Which of the following is the PRIMARY purpose of a dedicated AI system policy?

  A. Ensuring environmental impact is minimized
  B. Optimizing AI accuracy C. Providing a framework to set AI objectives
  C. Complying with external regulations
  C. Complying with external regulations

  ---

  Per AAISM, an AI policy is a governance instrument that defines objectives, principles, roles, responsibilities, accountability, and control requirements for AI systems across their lifecycle. It establishes the framework within which performance, compliance, ethics, risk appetite, security, privacy, and sustainability objectives are set and operationalized. Environmental considerations (A), accuracy optimization (B), and regulatory compliance (D) are important outcomes addressed under the policy, but the primary purpose is to provide the overarching framework for objectives and controls.

  References: AI Security Management (AAISM) Body of Knowledge - AI Governance Frameworks; Policies, Standards, and Procedures; Roles and Accountability in AI Programs.

• Question 274:

  When creating a use case for an AI model that provides sensitive decisions affecting end users, which of the following is the GREATEST benefit of using model cards?

  A. Ethical considerations of the model are documented
  B. Technical instructions for model deployment are created
  C. Data collection requirements are reduced
  D. Model type selection is documented
  A. Ethical considerations of the model are documented

  ---

  AAISM highlights that model cards are a governance tool designed to document ethical considerations, limitations, fairness constraints, data sources, and suitability of use cases for AI models-especially when they affect individuals' rights, opportunities, or access to services.

  Their greatest value is providing transparency and ethical clarity, ensuring stakeholders understand risks, bias considerations, and how decisions impact users.

  Deployment instructions (B) are not part of model cards. They do not reduce data needs (C), nor is model type selection (D) their primary purpose.

  References: AAISM Study Guide - AI Governance Documentation; Transparency and Model Cards.

• Question 275:

  Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?

  A. Deploy pre-trained models directly into production.
  B. Consolidate event logs for correlation and centralized analysis.
  C. Schedule periodic manual code reviews.
  D. Implement compartmentalization with least privilege enforcement.
  D. Implement compartmentalization with least privilege enforcement.

  ---

  The most effective strategy to minimize attack surfaces in AI agent security is to apply compartmentalization and least privilege enforcement.

  AAISM control frameworks emphasize: Isolation of components (e.g., training, inference, data pipelines) to limit lateral movement. Principle of least privilege to restrict access only to what is required for function. Hardening AI pipelines through segmentation rather than relying solely on manual reviews or monitoring.

  Pre-trained models and log centralization are useful but do not directly reduce the attack surface. Manual code

  reviews are important but insufficient against runtime exploitation.

  Thus, compartmentalization with least privilege enforcement is the most effective technical safeguard.