Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 1:

  Which of the following information is MOST important to include in a centralized AI inventory?

  A. Ownership and accountability of AI systems
  B. AI model use cases
  C. Training data sets
  D. Foundation model and package registry
  A. Ownership and accountability of AI systems

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  AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias,

  or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.

  References:

  AAISM Exam Content Outline - AI Governance and Program Management (AI Inventories and Oversight)

  AI Security Management Study Guide - Ownership and Accountability Structures

• Question 2:

  Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?

  A. Encrypting data in transit and at rest
  B. Conducting adversarial testing
  C. Implementing data sanitization techniques
  D. Enforcing least privilege access
  C. Implementing data sanitization techniques

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  AAISM materials make clear that the best safeguard against sensitive information being leaked through the outputs of LLMs is data sanitization. This involves filtering, redacting, or masking sensitive content before the model can use it, thereby preventing unintended disclosure in outputs. Encryption protects confidentiality in storage and transmission but does not stop output leaks. Adversarial testing helps identify vulnerabilities but does not prevent exposure by itself. Least privilege access restricts who can

  interact with the model but does not sanitize the content of its outputs. The control most directly tied to preventing leakage is implementing data sanitization techniques.

  References:

  AAISM Exam Content Outline - AI Technologies and Controls (Data Leakage Prevention)

  AI Security Management Study Guide - Sensitive Data Controls in Generative AI

• Question 3:

  An organization is adopting an agentic AI solution from an external vendor to support internal IT operations. Which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?

  A. Industry benchmarking peer review
  B. Third-party audit reports
  C. Internal red-team testing reports
  D. General AI security whitepapers
  B. Third-party audit reports

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  AAISM states that when evaluating external AI vendors, independently issued third-party audit reports (SOC, ISO, AI assurance assessments) provide the strongest evidence of implemented controls because they are objective, repeatable, and externally verified. Peer reviews (A) lack formality, internal red-team reports (C) are non-independent, and whitepapers (D) are marketing documents without assurance value.

  References: AAISM Study Guide - Third-Party AI Risk Management; Independent Assurance and Audit Requirements.

• Question 4:

  Which safeguard BEST limits the operational risk of hallucinations in LLM-driven customer-service tools?

  A. Parameter-efficient fine-tuning
  B. Human approval workflow for high-impact responses
  C. Model sharding
  D. Expanded context length
  B. Human approval workflow for high-impact responses

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  AAISM clearly states that hallucination mitigation for customer-facing or high-impact outputs requires human-in-the-loop gatekeeping. Technical adjustments (A, C, D) help, but do not replace human review.

  References:

  AAISM Study Guide ?HITL Controls; Hallucination Governance.

• Question 5:

  A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:

  A. Poisoning attacks
  B. Evasion attacks
  C. Membership inference
  D. Model exfiltration
  B. Evasion attacks

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  The AAISM study framework describes evasion attacks as attempts to manipulate or probe a trained model during inference by using crafted inputs that appear normal but cause the system to generate inconsistent or erroneous outputs. Contradictory results from nearly identical queries are a typical symptom of evasion, as the attacker is probing decision boundaries to find weaknesses. Poisoning attacks occur during training, not inference, while membership inference relates to exposing whether data was

  part of the training set, and model exfiltration involves extracting proprietary parameters or architecture. The clearest indication of contradictory outputs from similar queries therefore aligns directly with the definition of evasion attacks in AAISM materials.

  References:

  AAISM Study Guide - AI Technologies and Controls (Adversarial Machine Learning and Attack Types)

  ISACA AI Security Management - Inference-time Attack Scenarios

• Question 6:

  How can an organization best remain compliant when decommissioning an AI system that recorded patient data?

  A. Perform a post-destruction risk assessment
  B. Ensure backups are tested and access controls are audited
  C. Update governance policies based on lessons learned
  D. Ensure a certificate of destruction is received and archived
  D. Ensure a certificate of destruction is received and archived

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  AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems. A certificate of destruction ensures: proof of lawful data disposal auditability regulatory compliance defensibility during inspections Post-destruction risk assessments (A) are not primary compliance evidence. Backup tests (B) are operational tasks, not decommissioning proof. Policy updates (C) are future improvements.

  References: AAISM Study Guide - AI Decommissioning and Regulatory Compliance; Data Destruction Evidence.

• Question 7:

  A company suspects its AI threat-detection model is degrading due to changes in cyber-attack behavior. Which activity would MOST effectively identify the cause?

  A. Reviewing system prompts
  B. Conducting model drift analysis
  C. Increasing the training data size
  D. Performing access-control audits
  B. Conducting model drift analysis

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  AAISM identifies drift analysis as the primary method to detect whether the statistical properties of input or behavior patterns have shifted. Changes in attacker techniques often cause drift that lowers detection accuracy. Adding more data (C) without diagnosis is ineffective. Prompt review (A) is irrelevant for non-LLM models. Access audits (D) do not address performance degradation.

  References:

  AAISM Study Guide ?Drift Monitoringand; ML Lifecycle Performance Analysis.

• Question 8:

  Which of the following is the MOST effective use of AI in incident response?

  A. Streamlining incident response testing
  B. Automating incident response triage
  C. Improving incident response playbook
  D. Ensuring chain of custody
  B. Automating incident response triage

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  AAISM's risk management guidance notes that the most effective application of AI in incident response is in automating triage activities. AI systems can rapidly analyze logs, alerts, and telemetry to prioritize incidents, reducing response times and allowing human analysts to focus on critical issues. Streamlining testing and improving playbooks are valuable but secondary benefits. Ensuring chain of custody is critical for legal admissibility of evidence but is primarily a human and process-driven control, not AI's

  strength. The greatest efficiency and effectiveness comes from AI-driven triage automation.

  References:

  AAISM Exam Content Outline - AI Risk Management (AI for Incident Detection and Response)

  AI Security Management Study Guide - Automation in Security Operations

• Question 9:

  Which activity BEST supports ongoing assurance for AI systems operating in regulated industries?

  A. Security team certification renewal
  B. Continuous monitoring with compliance-aligned KPIs
  C. Annual re-documentation of the data pipeline
  D. Reducing manual sign-off requirements
  B. Continuous monitoring with compliance-aligned KPIs

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  AAISM requires continuous monitoring aligned to compliance criteria (e.g., fairness, transparency, output integrity, privacy metrics). Annual documentation (C) lacks timeliness. Certifications (A) are indirect. Reducing sign-offs (D) is risky.

  References:

  AAISM Study Guide ?Regulatory Monitoring; Compliance-Aligned Operational Metrics.

• Question 10:

  An organization is facing a deepfake attack intended to manipulate stock prices. The organization's crisis communication plan has been activated. Which of the following is MOST important to include in the initial response?

  A. Conduct employee awareness training on recognizing deepfake videos and audio
  B. Provide clarifying information in a pre-approved public statement
  C. Conduct a detailed forensic analysis to identify the source of the deepfake
  D. Engage with brand monitoring services to track social media activity
  B. Provide clarifying information in a pre-approved public statement

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  AAISM guidance on crisis management and communication emphasizes that the initial priority in responding to a reputational or market manipulation attack is to provide accurate clarifying information to the public through a pre-approved statement. This ensures stakeholders and markets are given verified facts immediately, limiting the spread of misinformation. While forensic analysis, employee training, and monitoring activities are important, they occur after the immediate need for public trust and damage

  control is addressed. Pre-approved statements are a central control in AI-related incident response to ensure consistency, timeliness, and credibility in communications.

  References:

  AAISM Study Guide - AI Governance and Program Management (Incident Response and Crisis Communication)

  ISACA AI Security Management - Public Communication and Trust Preservation