Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 161:

  Which of the following is the MOST effective mitigation against data-poisoning attacks targeting incremental learning pipelines?

  A. Real-time ingestion validation
  B. Encrypting the training database
  C. Increasing model batch size
  D. Reducing learning rate
  A. Real-time ingestion validation

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  Incremental learning is highly vulnerable because new data immediately influences model state. AAISM recommends gating ingestion with provenance checks, schema validation, anomaly screening, and label consistency. Encryption (B) protects confidentiality, not integrity.

  References:

  AAISM Study Guide ?Secure Data Ingestion; Poisoning Prevention Controls.

• Question 162:

  An organization plans to use AI to analyze the shopping patterns of its customers to predict interests and send targeted, customized marketing emails. Which of the following should be done FIRST?

  A. Obtain customer consent
  B. Train the marketing department
  C. Update the terms of service
  D. Verify customer email addresses
  A. Obtain customer consent

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  The first action, before any processing of personal data for AI-driven profiling and targeted communications, is to establish a lawful basis for processing. Under AAISM-aligned privacy governance, explicit and informed consent is prioritized for new or sensitive uses such as interest profiling and targeted marketing. Consent ensures purpose limitation, transparency, and user control prior to model ingestion and campaign activation. Training teams, updating terms of service, or verifying contact details are important, but they do not provide legal authority to process data; therefore, they follow after consent is obtained.

  References: AI Security Management (AAISM) Body of Knowledge - Privacy Governance and Lawful Basis; Purpose Limitation and Transparency; Consent Management in AI-enabled Marketing. AAISM Study Guide - Data Protection Controls for AI Profiling; Consent Capture and Record-Keeping.

• Question 163:

  Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models used for fraud detection systems?

  A. Reducing computational resources
  B. Enhancing the accuracy of predictions
  C. Protecting individual data contributions while allowing statistical analysis
  D. Increasing model training speed
  C. Protecting individual data contributions while allowing statistical analysis

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  AAISM defines differential privacy as a technique that ensures individual data points cannot be reverse-engineered or identified, even when used to train high-risk AI models such as fraud detection systems. Its primary purpose is to protect individual privacy while maintaining aggregate utility. Accuracy improvements (B) or performance optimizations (A, D) are not core objectives of differential privacy.

  References: AAISM Study Guide - Privacy-Preserving Machine Learning; Differential Privacy Fundamentals.

• Question 164:

  An AI system accesses multiple confidential datasets to produce internal reports. Which control BEST prevents cross-dataset leakage?

  A. Token-level encryption
  B. Data siloing with separated context boundaries
  C. Larger transformer context windows
  D. Model quantization
  B. Data siloing with separated context boundaries

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  AAISM identifies data siloing and strict access-segregation as the most efficient way to prevent cross-context leakage. Encryption (A) protects data but does not separate access. Context windows (C) increase leak risk. Quantization (D) is performance-related.

  References:

  AAISM Study Guide ?Data Segregationand; Boundary Controls.

• Question 165:

  Which of the following is MOST important for effective AI risk management? A. Utilization of best practice AI risk management frameworks

  A. Internal stakeholder participation in AI risk management processes
  B. Risk measurement during an early stage of the AI system life cycle
  C. Creation of separate risk management processes for AI-specific risk
  C. Creation of separate risk management processes for AI-specific risk

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  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.

• Question 166:

  Which of the following is the MOST effective use of AI-enabled tools in a security operations center (SOC)?

  A. Employing AI-enabled tools to reduce false negatives by detecting subtle attack patterns
  B. Using AI-enabled tools exclusively to classify all types of security incidents
  C. Replacing human analysis with automated AI decision-making processes
  D. Assigning AI-enabled tools to triage non-critical alerts to preserve SOC resources
  A. Employing AI-enabled tools to reduce false negatives by detecting subtle attack patterns

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  The most effective SOC application of AI is in detecting subtle, hard-to-find attack patterns that reduce false negatives. AAISM technical control guidance notes that AI in SOCs is best applied to: Enhance detection accuracy and sensitivity to anomalies. Assist analysts in identifying hidden patterns that traditional rule-based systems miss.

  Augment-not replace-human decision-making for high-confidence outcomes.

  Options B and C incorrectly shift responsibility entirely to AI, which contradicts governance principles

  requiring human oversight. Option D is useful for efficiency, but the primary effectiveness comes from

  improving detection quality.

  Therefore, the most effective use is to reduce false negatives and detect subtle attacks.

• Question 167:

  A critical AI system shows biased outcomes. What is the BEST course of action?

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

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  AAISM emphasizes root cause analysis as the primary step when AI behavior deviates unexpectedly. RCA determines whether issues stem from: data drift model drift poisoned data missing features logic errors configuration changes Retraining (D) may be required later, but only after RCA. Kill switches (A) are last-resort tools. Audits (B) help but are narrower than RCA.

  References: AAISM Study Guide - Incident Response for AI; Root Cause First Principle.

• Question 168:

  Which of the following recommendations would BEST help a service provider mitigate the risk of lawsuits arising from generative AI's access to and use of internet data?

  A. Activate filtering logic to exclude intellectual property flags
  B. Disclose service provider policies to declare compliance with regulations
  C. Appoint a data steward specialized in AI to strengthen security governance
  D. Review log information that records how data was collected
  A. Activate filtering logic to exclude intellectual property flags

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  The AAISM materials highlight that one of the primary legal risks with generative AI systems is the unauthorized use of copyrighted or intellectual property-protected data drawn from internet sources. To mitigate lawsuits, the most effective recommendation is to implement filtering logic that actively excludes data flagged for intellectual property risks before ingestion or generation. While disclosing compliance policies, appointing governance roles, or reviewing logs are supportive measures, they do not directly

  prevent the core liability of using restricted content. The study guide explicitly emphasizes that proactive filtering and data governance controls are the most effective safeguards against legal disputes concerning content origin.

  References:

  AAISM Exam Content Outline - AI Risk Management (Legal and Intellectual Property Risks)

  AI Security Management Study Guide - Generative AI Data Governance

• Question 169:

  Which of the following BEST enables an organization to strengthen information security controls around the use of generative AI applications?

  A. Ensuring controls exceed industry benchmarks
  B. Monitoring AI outputs against policy
  C. Validating AI model training data
  D. Implementing a kill switch
  B. Monitoring AI outputs against policy

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  For generative AI, the primary enterprise security exposure is data and content exfiltration or policy violations at output, including leakage of sensitive data, toxic content, or regulatory non-compliance. AAISM prescribes policy-aligned output monitoring (e.g., DLP checks, PII/PHI detection, toxicity/safety filters, watermark / attribution checks) integrated into inference gateways to enforce organizational policies and evidence compliance. Exceeding benchmarks (A) is not a control; training-data validation (C) may be infeasible with third-party LLMs; and kill switches (D) are essential contingency controls but do not continuously strengthen everyday security posture.

  References: AI Security Management (AAISM) Body of Knowledge - GenAI Governance and Guardrails; Output Filtering and DLP Controls; Policy Enforcement at Inference. AAISM Study Guide -Monitoring and Auditing of GenAI; Gateway Patterns for Safe Use; Control Effectiveness Measures.

• Question 170:

  Which of the following strategies is the MOST effective way to protect against AI data poisoning?

  A. Ensuring the model is trained on diverse data sources
  B. Increasing model complexity
  C. Using robust data validation techniques and anomaly detection
  D. Incorporating more features and data into model training
  C. Using robust data validation techniques and anomaly detection

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  AAISM identifies robust data validation and anomaly detection on incoming training data as the primary defense against data poisoning. These controls detect corrupted, manipulated, or adversarial samples before they enter the training pipeline. Diverse data (A) is helpful but not protective against poisoning. More complexity (B) does not mitigate poisoning and can worsen vulnerability. More features (D) increases attack surface.

  References: AAISM Study Guide - AI Threats; Data Poisoning Mitigation through Validation and Anomaly Detection.