Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 231:

  An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?

  A. Segmenting AI services across containers to manage resource constraints
  B. Restricting access to AI models using IP allow lists to reduce public exposure
  C. Integrating differential privacy mechanisms into model training to limit data leakage
  D. Applying threat modeling specific to AI components before deployment
  D. Applying threat modeling specific to AI components before deployment

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  Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.

  References: AI Security Management (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture and Control Selection; Risk-Based Design Assurance. AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.

• Question 232:

  A newly hired programmer suspects that the organization's AI solution is inferring users' sensitive information and using it to advise future decisions. Which of the following is the programmer's BEST course of action?

  A. Conduct a code review
  B. Alert the CIO to the risk
  C. Suggest fine-tuning the AI solution
  D. Inform the governance panel
  D. Inform the governance panel

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  AAISM directs personnel to use established AI governance channels for suspected privacy, ethics, or compliance risks. The governance panel (risk, privacy, legal/compliance, security, product/data science) is chartered to triage, record, investigate, and direct remediation for potential inference of sensitive attributes and resulting decision impacts. Direct technical action (A or C) bypasses due process and accountability; escalating directly to a single executive (B) lacks the structured, cross-functional oversight

  required for regulated and ethical AI risk handling.

  References:AI Security Management (AAISM) Body of Knowledge: AI Governance Operating Model; Roles and Responsibilities; Risk Intake and Triage for Privacy/Inference Risks.AAISM Study Guide: Ethics and Responsible AI Escalation Pathways; Governance Board Procedures; Documentation and Decision Records.

• Question 233:

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

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

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  The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization' s established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance. While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to-and can be verified against-the control objectives that define the program's intended outcomes.

  References: AI Security Management (AAISM) Body of Knowledge: AI Governance and Program Management -Security program alignment, control objectives, and requirements traceability AI Security Management Study Guide: Control objective mapping, architecture fit criteria, and solution selection governance

• Question 234:

  An aerospace manufacturing company that prioritizes accuracy and security has decided to use generative AI to enhance operations. Which of the following large language model (LLM) adoption plans BEST aligns with the company's risk appetite?

  A. Developing a public LLM to automate critical functions
  B. Purchasing an LLM dataset on the open market
  C. Contracting LLM access from a reputable third-party provider
  D. Developing a private LLM to automate non-critical functions
  D. Developing a private LLM to automate non-critical functions

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  AAISM recommends aligning AI adoption with organizational risk appetite by limiting blast radius, protecting sensitive data, and staging adoption in lower-risk domains first. Building a private LLM for noncritical functions preserves data control, enables tighter governance (access control, logging, evaluation), and confines any model errors away from safety- or mission-critical operations. A public LLM for critical functions (A) is misaligned with a high-assurance posture; buying open-market datasets (B) raises provenance and licensing risk; third-party access (C) can be appropriate but still introduces vendor/visibility limits and data residency concerns that may not meet aerospace security needs.

  References: AI Security Management (AAISM) Body of Knowledge - Risk Appetite Mapping to AI Use Cases; Criticality Segmentation; Data Control and Deployment Models. AAISM Study Guide - Phased Adoption for High-Assurance Environments; Private vs. Hosted LLM Trade-offs; Governance, Evaluation, and Containment Patterns.

• Question 235:

  What BEST protects trade secrets related to AI technologies during their life cycle?

  A. Enforcing trademark rights
  B. Restricting access to sensitive data
  C. Patenting AI algorithms and data
  D. Watermarking AI output
  B. Restricting access to sensitive data

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  AAISM emphasizes access control and data security as the strongest mechanisms to protect trade secrets, including: . proprietary algorithms . training datasets . model weights . internal design documentation Trademarks (A) protect brand, not trade secrets. Patents (C) require public disclosure. Watermarks (D) protect generated content, not internal trade secrets.

  References: AAISM Study Guide - AI Intellectual Property Protection and Access Controls.

• Question 236:

  An organization is deploying an automated AI cybersecurity system. Which strategy MOST effectively minimizes human error and improves security?

  A. Manual monitoring of alerts
  B. Using historical data to train detection software
  C. Utilizing machine learning algorithms to ensure responsible use
  D. Conducting periodic penetration testing
  B. Using historical data to train detection software

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  AAISM states that the effectiveness of automated AI cybersecurity systems depends heavily on well-trained detection models using high-quality historical attack data. Historical data improves: . detection accuracy . reduction of false positives . reduction of human misinterpretation Manual monitoring (A) increases human error. ML "ensuring responsibility" (C) is not a defined control. Pen testing (D) does not reduce human mistakes.

  References: AAISM Study Guide - AI in Cybersecurity; Model Training for Threat Detection.

• Question 237:

  When documenting information about machine learning (ML) models, which of the following artifacts BEST helps enhance stakeholder trust?

  A. Hyperparameters
  B. Data quality controls
  C. Model card
  D. Model prototyping
  C. Model card

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  The model card is a governance artifact that communicates intended use, performance characteristics,

  limitations, fairness considerations, and ethical notes of an ML model.

  AAISM governance materials highlight that stakeholder trust comes from transparency and explainability.

  While hyperparameters and data quality controls are technical details, they lack stakeholder-facing clarity.

  Model prototyping is part of development but not a governance record.

  Model cards are explicitly recommended to:

  Provide explainability and context for decision-making.

  Demonstrate governance, transparency, and compliance.

  Enable stakeholders (regulators, auditors, business leaders) to trust the system. Therefore, the model card is the artifact that best enhances trust.

• Question 238:

  Which of the following is the MOST critical key risk indicator (KRI) for an AI system?

  A. The accuracy rate of the model
  B. The amount of data in the model
  C. The response time of the model
  D. The rate of drift in the model
  D. The rate of drift in the model

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  AAISM highlights that while accuracy and performance metrics are important, the rate of drift is the most critical KRI for AI systems. Model drift occurs when input data or environmental conditions shift, causing the system to degrade and produce unreliable outputs. This risk indicator directly reflects whether the AI continues to function as intended over time. Accuracy rates and response times are performance metrics, not primary risk signals. The amount of data in the model does not reliably indicate

  exposure to risk. Therefore, the greatest KRI for ongoing assurance and governance is the rate of drift.

  References:

  AAISM Study Guide - AI Risk Management (Monitoring and Drift Detection)

  ISACA AI Security Management - Key Risk Indicators for AI Systems

• Question 239:

  An organization implementing a large language model (LLM) application notices significant and unexpected cost increases due to excessive computational resource usage. Which vulnerability is MOST likely in need of mitigation?

  A. Excessive agency
  B. Sensitive information disclosure
  C. System prompt leakage
  D. Unbounded consumption
  D. Unbounded consumption

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  AAISM highlights unbounded consumption (token/payment exhaustion, unmetered tool calls, prompt bombs) as a key LLM risk affecting cost and availability. Controls include request quotas, max tokens, rate-limits, budget guards, circuit breakers, and cost-aware routing. Excessive agency (A) relates to unsupervised actions; sensitive disclosure (B) and prompt leakage (C) are confidentiality risks, not primary drivers of runaway compute spend.

  References: AI Security Management. (AAISM) Body of Knowledge - LLM Risk Taxonomy (Abuse and Cost Risks); Guardrails: Rate-Limiting, Quotas, and Budget Controls; Resilience and Cost-Containment Patterns.

• Question 240:

  An organization has requested a developer to apply AI algorithms to existing modules in order to improve customer service quality. At this stage, which of the following should be considered FIRST?

  A. The developer may need to be held accountable for business inquiries raised by customers
  B. IT management may need to revise the service agreement if AI behavior cannot be predefined
  C. Project sponsors may need to agree on a phased approach in order to ensure safe release
  D. The organization may need to explain the performance of the applied AI algorithm
  B. IT management may need to revise the service agreement if AI behavior cannot be predefined

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  According to AAISM governance principles, when AI functionality is added to existing services, the first consideration is contractual and service-level accountability. If AI outputs cannot be predefined, the existing service agreements may no longer reflect performance responsibilities or liability. Revising or updating the agreement ensures governance alignment, accountability, and risk management for AI-driven behavior. Phased approaches and performance explanations are valuable but occur later in project

  management. Developer accountability for customer inquiries is not a primary governance step. The most immediate consideration is revising service agreements when AI introduces new uncertainties.

  References:

  AAISM Exam Content Outline - AI Governance and Program Management (Policies and Service Agreements)

  AI Security Management Study Guide - Accountability in AI Deployments