Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 11:

  AI developers often find deep learning systems difficult to explain PRIMARILY because:

  A. Knowledge dynamically changes without logs
  B. Neural network architectures include statistical methods not fully understood
  C. Algorithms rely on probability theories
  D. Training data is spread across public domains
  B. Neural network architectures include statistical methods not fully understood

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  AAISM notes that deep learning systems lack transparency due to complex neural architectures, where internal representations are statistical, nonlinear, and not directly interpretable. While probability (C) and data sourcing (D) contribute to opacity, the root cause is the intrinsic complexity and opacity of deep neural networks.

  References: AAISM Study Guide - Explainability Challenges in Deep Learning.

• Question 12:

  When evaluating a third-party AI service provider, which of the following master services agreement provisions is MOST critical for managing security risk?

  A. Prohibiting the use of customer data for model training
  B. Restricting query volume thresholds
  C. Sharing real-time log information
  D. Guaranteeing unlimited model retraining requests
  A. Prohibiting the use of customer data for model training

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  The most material contractual control for reducing security and privacy risk in outsourced AI services is a data-use restriction that prohibits the provider from using customer data for model training (and from derivative model improvements) unless explicitly authorized. This prevents unintended secondary processing, model inversion exposure of proprietary data, unauthorized profiling, and downstream data proliferation across multi-tenant systems. AAISM positions third-party risk controls to prioritize data minimization, purpose limitation, confidentiality, and downstream controls; among common MSA provisions, data-use limitations directly constrain the provider's technical and organizational handling of sensitive inputs, making it the highest-impact risk-reducing clause. Query throttling (B) and logging (C) are useful operational controls but are secondary to legal/processing authority. Unlimited retraining (D) increases attack surface and cost without addressing the core risk of misuse of customer data.

  References: AI Security Management (AAISM) Body of Knowledge - Third-Party and Supply-Chain Governance; Contractual Controls for AI Services; Data Minimization and Purpose Limitation. AAISM Study Guide - Procurement and MSA/DPA Clauses for AI; Provider Model Training and Data-Use Restrictions; Privacy and Confidentiality Safeguards in Outsourced AI.

• Question 13:

  An organization develops and implements an AI-based plug-in for users that summarizes their individual emails. Which of the following is the GREATEST risk associated with this application?

  A. Lack of application vulnerability scanning
  B. Data format incompatibility
  C. Insufficient rate limiting for APIs
  D. Inadequate controls over parameters
  D. Inadequate controls over parameters

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  According to AAISM risk management guidance, the greatest risk in AI applications handling personal communication data is inadequate parameter controls, which may allow unintended access, manipulation, or leakage of sensitive information. Plug-ins that interact with emails must enforce strict parameter validation and security restrictions to prevent unauthorized or manipulated inputs. While vulnerability scanning, format incompatibility, and API rate limiting are valid concerns, they are secondary. The

  primary risk is a lack of strong parameter controls that could expose sensitive content.

  References:

  AAISM Exam Content Outline - AI Risk Management (Application Security Risks)

  AI Security Management Study Guide - Plug-in and API Security Risks

• Question 14:

  To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:

  A. The organization has implemented a responsible development policy
  B. Outputs of AI tools do not perpetuate adverse biases
  C. Stakeholders have approved alignment with company values
  D. AI tools are evaluated by the privacy department before implementation
  B. Outputs of AI tools do not perpetuate adverse biases

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  AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.

  References:

  AAISM Study Guide - AI Risk Management (Bias and Ethics Validation)

  ISACA AI Security Management - Ethical AI Practices

• Question 15:

  An organization utilizes AI-enabled mapping software to plan routes for delivery drivers. A driver following the AI route drives the wrong way down a one-way street, despite numerous signs. Which of the following biases does this scenario demonstrate?

  A. Selection
  B. Reporting
  C. Confirmation
  D. Automation
  D. Automation

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  AAISM defines automation bias as the tendency of individuals to over-rely on AI-generated outputs even when contradictory real-world evidence is available. In this scenario, the driver ignores traffic signs and follows the AI's instructions, showing blind reliance on automation. Selection bias relates to data sampling, reporting bias refers to misrepresentation of results, and confirmation bias involves interpreting information to fit pre-existing beliefs. The most accurate description is automation bias.

  References:

  AAISM Exam Content Outline - AI Risk Management (Bias Types in AI)

  AI Security Management Study Guide - Automation Bias in AI Use

• Question 16:

  An AI compliance officer evaluates whether a vendor's training dataset complies with privacy laws. Which review must occur FIRST?

  A. Assessment of retention periods
  B. Verification of lawful basis for data collection
  C. Testing re-identification resistance
  D. Examining data labeling processes
  B. Verification of lawful basis for data collection

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  AAISM mandates establishing lawful basis before handling or processing personal data. Retention (A), re-ID testing (C), and labeling quality (D) come after confirming legal grounds.

  References:

  AAISM Study Guide ?Privacy Governance; Lawful Basisand; Data Handling.

• Question 17:

  Which defense is MOST effective against cyberattacks that alter input data to avoid detection?

  A. Enhancing model robustness through adversarial training
  B. Restricting access to internal model parameters
  C. Conducting periodic monitoring of decisions
  D. Applying differential privacy to training data
  A. Enhancing model robustness through adversarial training

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  AAISM lists adversarial training as the strongest method to harden models against input manipulation attacks. By exposing models to adversarial examples during training, the system learns to resist evasion techniques. Access restriction (B) protects confidentiality, not detection evasion. Monitoring (C) is reactive, not preventive. Differential privacy (D) protects individual data, not adversarial inputs.

  References: AAISM Study Guide - AI Evasion Attacks; Adversarial Training Mitigation.

• Question 18:

  An organization is implementing an AI-based credit assessment engine using internal and third-party customer data. Which of the following BEST aligns with data management controls for the AI life cycle?

  A. Documented procedures for data sourcing, lineage tracking, and quality validation
  B. Use of hashed identifiers to anonymize datasets used for model validation and internal analytics
  C. Encrypted isolation and dynamic access controls on training data pipelines
  D. Limitation of model training to structured data from vetted sources to minimize ingestion risk
  A. Documented procedures for data sourcing, lineage tracking, and quality validation

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  AAISM emphasizes that data governance over the full AI life cycle is foundational. The official content describes effective AI data management as including documented procedures for: (1) how data is sourced, (2) how lineage is tracked from origin to model, and (3) how data quality is validated and monitored. This ensures transparency, accountability, and auditability, which are especially critical in regulated areas like credit assessments. While hashing identifiers (B) and encryption/access controls (C) are important privacy and security mechanisms, they are partial controls within a broader governance framework and do not, on their own, establish end-to-end life-cycle management. Limiting training to structured data (D) is a design choice and may reduce risk but is neither sufficient nor required as a best practice. Option A directly reflects AAISM' s prescribed governance controls for AI data throughout its life cycle.

  References: AI Security Management. (AAISM) Study Guide - AI Data Governance and Life Cycle Management; Data Lineage and Quality Assurance.

• Question 19:

  Which AI data management technique involves creating validation and test data?

  A. Learning
  B. Splitting
  C. Training
  D. Annotating
  B. Splitting

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  AAISM describes data splitting as the process of dividing datasets into: . training . validation . test sets This is essential for reducing overfitting and ensuring robust evaluation. Learning (A) refers to model training. Annotating (D) labels data. Training (C) does not create validation/test

  data.

  References: AAISM Study Guide - AI Data Preparation and Dataset Splitting.

• Question 20:

  Which of the following is the GREATEST benefit of performing AI security risk assessments?

  A. Appropriate privacy risk controls are implemented for AI models
  B. The appropriate level of funding is secured for AI security risk
  C. The risk register is updated with the latest AI risk
  D. Risk prioritization decisions are made for AI security
  D. Risk prioritization decisions are made for AI security

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  AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls. Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.

  References: AI Security Management. (AAISM) Body of Knowledge - AI Risk Assessment and Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.