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[Hardware] Free PDF Quiz ISACA - AAISM - ISACA Advanced in AI Security Management (AAISM) E

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【Hardware】 Free PDF Quiz ISACA - AAISM - ISACA Advanced in AI Security Management (AAISM) E

Posted at before yesterday 18:44      View：12 | Replies：1        Print      Only Author   [Copy Link] 1#
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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q252-Q257):NEW QUESTION # 252
When robust input controls cannot prevent prompt injections in an LLM, what is the BEST compensating control?
  • A. Review and annotate the AI system's outputs
  • B. Implement identity and access management (IAM)
  • C. Fine-tune the system to validate inputs
  • D. Conduct human reviews of AI system inputs
Answer: A
Explanation:
AAISM identifies output review and annotation as the most practical compensating control when robust input validation cannot be applied.
Output moderation detects:
* maliciously influenced responses
* unsafe outputs
* security-policy violations
IAM (B) does not mitigate prompt injection itself. Human review of inputs (C) is unrealistic at scale. Fine- tuning (A) cannot guarantee full prevention.
References: AAISM Study Guide - Generative AI Safeguards; Output Moderation Controls.

NEW QUESTION # 253
A financial organization is concerned about the risk of prompt injection attacks on its customer service chatbot. Which of the following controls BEST addresses this concern?
  • A. Input validation
  • B. Human-in-the-loop
  • C. Continuous monitoring
  • D. Increasing model parameters
Answer: A
Explanation:
AAISM emphasizes preventive technical controls for LLM threats such as prompt injection, including input validation/sanitization, instruction isolation, allow/deny lists, context segmentation, and output filtering.
These reduce the model's exposure to adversarial instructions embedded in user prompts or retrieved context.
Monitoring (A) is detective, not preventive; increasing parameters (B) does not inherently improve security against injection; human-in-the-loop (D) is valuable for high-risk decisions but does not directly neutralize injection vectors at the control boundary the way input validation and content filtering do.
References: AI Security Management (AAISM) Body of Knowledge - Technical Controls for LLM Security; Input/Output Filtering and Context Isolation; Secure Inference and Prompt Injection Mitigations.

NEW QUESTION # 254
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. Disclose service provider policies to declare compliance with regulations
  • B. Appoint a data steward specialized in AI to strengthen security governance
  • C. Review log information that records how data was collected
  • D. Activate filtering logic to exclude intellectual property flags
Answer: D
Explanation:
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

NEW QUESTION # 255
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 organization may need to explain the performance of the applied AI algorithm
  • B. Project sponsors may need to agree on a phased approach in order to ensure safe release
  • C. The developer may need to be held accountable for business inquiries raised by customers
  • D. IT management may need to revise the service agreement if AI behavior cannot be predefined
Answer: D
Explanation:
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

NEW QUESTION # 256
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. Applying threat modeling specific to AI components before deployment
  • B. Segmenting AI services across containers to manage resource constraints
  • C. Restricting access to AI models using IP allow lists to reduce public exposure
  • D. Integrating differential privacy mechanisms into model training to limit data leakage
Answer: A
Explanation:
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 & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.

NEW QUESTION # 257
......
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