Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 171:

  Which of the following is MOST important for an organization to consider when implementing a preventive security safeguard into a new AI product?

  A. Input sanitization
  B. Model output monitoring
  C. Penetration testing
  D. Differential privacy
  A. Input sanitization

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  AAISM materials emphasize that the most effective preventive safeguard is to ensure input sanitization. Preventive controls stop malicious or malformed inputs from reaching the model in the first place, thereby reducing the likelihood of prompt injection, evasion, or poisoning at inference time. Model output monitoring is a detective control, not preventive. Penetration testing is an assessment technique rather than a safeguard. Differential privacy protects data privacy but does not prevent adversarial input

  manipulation. Therefore, the most important preventive safeguard in a new AI product is robust input sanitization.

  References:

  AAISM Study Guide - AI Technologies and Controls (Preventive vs. Detective Safeguards)

  ISACA AI Security Management - Input Validation in AI Systems

• Question 172:

  A security assessment revealed that attackers could access sensitive company data through chat interface injection. What is the BEST mitigation?

  A. Conducting regular security audits
  B. Manually reviewing AI model outputs
  C. Implementing input validation and templates
  D. Ensuring continuous monitoring and tagging
  C. Implementing input validation and templates

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  AAISM explains that prompt injection attacks are best mitigated by: . strict input validation . templated prompts . controlled context windows . guardrail enforcement These prevent malicious instructions from overriding system prompts. Audits (A) are periodic, not preventive. Manual review (B) is not scalable. Monitoring (D) detects issues but

  does not block injection.

  References: AAISM Study Guide - Prompt Injection and Input Control Mechanisms.

• Question 173:

  An organization has implemented a natural language processing model to respond to customer questions when personnel are not available. A pre-implementation security assessment revealed attackers could access sensitive company data through a chat interface injection attack. Which of the following is the BEST way to prevent this attack?

  A. Ensuring continuous monitoring and data tagging
  B. Manually reviewing AI model outputs
  C. Implementing input validation and templates
  D. Conducting regular information security audits
  C. Implementing input validation and templates

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  To prevent prompt/interface injection, AAISM prioritizes preventive technical controls at the boundary: input validation/sanitization, structured templates/system prompts, allow/deny lists, and context isolation. These measures constrain user-supplied content and block adversarial instructions from being interpreted as system directives. Monitoring (A) and audits (D) are detective/assurance activities; manual output review (B) is compensating but less scalable and does not prevent injection.

  References: AI Security Management. (AAISM) Body of Knowledge - Secure Prompting and Input Controls; Interface Injection Mitigations; Context and Instruction Isolation Patterns.

• Question 174:

  Which of the following is the BEST way to reduce the risk of misuse of an AI agent that has access to critical data and systems?

  A. Validate agent compliance with output restrictions
  B. Allow users to configure the agent for productivity
  C. Prohibit users from manipulating agent behavior
  D. Limit human review of AI decisions
  A. Validate agent compliance with output restrictions

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  AAISM frameworks highlight output-based controls, including output filtering, restriction validation, and policy-aligned guardrails as primary defenses for AI agents with high privileges. Ensuring the agent does not output unauthorized instructions or sensitive data directly mitigates misuse. Allowing user configuration (B) increases risk. Prohibiting manipulation entirely (C) is impractical. Reducing human oversight (D) increases system abuse potential.

  References: AAISM Study Guide - AI Agents, Output Controls, and Guardrails.

• Question 175:

  An organization is designing an AI-based credit risk assessment system integrating sensitive financial data. Which option BEST supports security-by-design?

  A. Integrating differential privacy mechanisms into model training
  B. Applying threat modeling specific to AI components before deployment
  C. Segmenting AI services across containers
  D. Restricting access to AI models using IP allow lists
  B. Applying threat modeling specific to AI components before deployment

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  AAISM identifies AI-specific threat modeling as an essential early-stage control in security-by-design, particularly for high-risk systems like credit scoring. It systematically identifies: data poisoning bias vulnerabilities model evasion model extraction misuse scenarios Differential privacy (A) is powerful but is a mitigation, not the overarching design control. Segmentation (C) and IP allow lists (D) are supporting controls but not the foundational step in secure design.

  References: AAISM Study Guide - Security-by-Design; AI Threat Modeling.

• Question 176:

  When addressing privacy concerns related to AI systems, which of the following is the GREATEST significance of user consent for an organization?

  A. It helps the organization detect biases and ensure fairness
  B. It enables users to delete and modify their personal data
  C. It prevents unauthorized access to data within the AI system
  D. It allows the organization to process user data in the AI system
  D. It allows the organization to process user data in the AI system

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  Within AAISM's privacy governance, consent is a lawful basis that authorizes processing of personal data for defined purposes. Its principal significance is granting the organization the authority to process user data in AI workflows in line with stated purposes and limits. While fairness (A) and security controls (C) are essential, they are distinct obligations; data subject rights such as rectification/erasure (B) exist regardless of consent and are not "enabled" by it. Therefore, the greatest significance of consent is that it legally permits processing under declared purposes and constraints.

  References: AI Security Management. (AAISM) Body of Knowledge - Privacy and Data Protection in AI; Lawful Basis and Purpose Limitation. AAISM Study Guide - Consent, Transparency, and Data Subject Rights; Privacy-by-Design Controls in AI Pipelines.

• Question 177:

  Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

  A. Ensure backups are tested and access controls are recorded and audited to ensure compliance
  B. Update governance policies based on lessons learned and ensure a feedback loop exists
  C. Perform a post-destruction risk assessment to verify that there is no residual exposure of data
  D. Ensure the certificate of destruction is received and archived in line with data retention policies
  D. Ensure the certificate of destruction is received and archived in line with data retention policies

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  For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.

  References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning and Secure Disposal; Records Retention and Evidence of Destruction.

• Question 178:

  An organization is implementing AI agent development across multiple engineering teams. Which of the following is the MOST important focus of AI-specific security training for developers?

  A. Prompt injection, agent memory control, and insecure tool execution
  B. Dataset bias, explainability, and fairness in model decisions
  C. Output moderation, hallucination handling, and policy alignment
  D. API abuse, data leakage, and third-party plug-in risk
  A. Prompt injection, agent memory control, and insecure tool execution

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  For developer-facing, near-term hardening of AI agents, AAISM prioritizes secure agent design and runtime controls against prompt injection, unsafe memory/tool use, and tool-execution compromise. These are primary exploitation paths for agents that read external content, persist memory, and call tools with elevated privileges. Training must center on: guarding tool invocation, constraining memory scope, sanitizing /validating inputs, and isolating high-risk actions. Topics like bias/fairness (B) and policy/

  hallucinations (C) are important but are governance/assurance concerns; API abuse and plug-in risk (D) matter, yet the core, developer-controlled attack surface for agents is injection and unsafe tool/memory design.

  References:. AI Security Management. (AAISM) Body of Knowledge: Agent Security-prompt injection defenses, tool execution constraints, memory governance. AAISM Study Guide: Developer controls for agent frameworks; input validation, sandboxing, scoped permissions, guardrail patterns

• Question 179:

  An organization deploying an LLM is concerned input manipulations could compromise security. What is the MOST effective way to determine an acceptable risk threshold?

  A. Deploy real-time logging and monitoring
  B. Restrict all inputs containing special characters
  C. Assess the business impact of known threats
  D. Implement a static threshold limiting LLM outputs
  C. Assess the business impact of known threats

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  AAISM instructs that acceptable risk thresholds must be determined using business impact analysis. This aligns with the broader enterprise risk management principle of defining tolerances based on: potential harm regulatory exposure financial impact operational disruption Monitoring (A) detects attacks but does not set thresholds. Blocking special characters (B) is unrealistic and overly restrictive. Static thresholds (D) ignore business context and practicality.

  References: AAISM Study Guide - AI Risk Appetite and Threshold Determination.

• Question 180:

  Which of the following should be the MOST important consideration when conducting an AI impact assessment?

  A. Achieve business objectives
  B. Effect on employee retention
  C. Security awareness training
  D. Reputation of the organization
  A. Achieve business objectives

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  AAISM defines an AI Impact Assessment (AIIA) as a structured evaluation designed to determine whether an AI system can safely and responsibly support business objectives without creating unacceptable risks.

  The framework states that alignment to business objectives is the central purpose of AI adoption and therefore must be the starting point in an impact assessment.

  Reputation (D) is a consideration, but it is a secondary outcome of failing to meet objectives responsibly. Employee retention (B) and training (C) are not core drivers of an AIIA.

  References: AAISM Study Guide - AI Impact Assessment; Business Alignment Principles.