Title: Quiz 2026 ISACA AAISM: ISACA Advanced in AI Security Management (AAISM) Exam Per [Print This Page] Author: jamesgr537 Time: 5 hour before Title: Quiz 2026 ISACA AAISM: ISACA Advanced in AI Security Management (AAISM) Exam Per 2026 Latest ITexamReview AAISM PDF Dumps and AAISM Exam Engine Free Share: https://drive.google.com/open?id=1Rk-0PY1OMuZfG2D6NQgeDNGQRrA8pcdu
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2026 AAISM ¨C 100% Free Valid Exam Cram | Updated Valid AAISM Exam NotesIf you are worrying about that there is no enough time to prepare for AAISM exam, or you can't find the authoritative study materials about AAISM exam, but when you read this article, your worries will be deleted completely. The latest AAISM exam review materials offered by our ITexamReview will help you complete the AAISM Exam Preparation in short time. We have the authority of the exam materials and experienced team with rich sense of responsibility. All that we have done is just to help you easily pass the AAISM exam. ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q105-Q110):NEW QUESTION # 105
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. Unbounded consumption
B. Sensitive information disclosure
C. System prompt leakage
D. Excessive agency
Answer: A
Explanation:
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 & Cost Risks); Guardrails: Rate-Limiting, Quotas, and Budget Controls; Resilience and Cost-Containment Patterns.
NEW QUESTION # 106
An organization is deploying a large language model (LLM) and is concerned that input manipulations may compromise its integrity. Which of the following is the MOST effective way to determine an acceptable risk threshold?
A. Deploy a real-time logging and monitoring system
B. Restrict all user inputs containing special characters
C. Implement a static risk threshold by limiting LLM outputs
D. Assess the business impact of known threats
Answer: D
Explanation:
AAISM requires that risk thresholds/tolerances be set by aligning threat likelihood and impact with the organization's business context and risk appetite. Determining "acceptable" risk starts with assessing business impact of credible threats (e.g., prompt injection leading to data exfiltration, policy evasion, or harmful actions), then translating this into control intensity and thresholds. Hard input restrictions (A) and static output caps (C) are blunt measures that may degrade utility without ensuring alignment to risk appetite.
Monitoring (B) is essential for detection, but it does not, by itself, define what level of risk is acceptable.
References: AI Security Management (AAISM) Body of Knowledge - Risk Appetite and Tolerance for AI; Threat Modeling for LLMs; Business Impact Analysis and Risk Acceptance Criteria.
NEW QUESTION # 107
Which approach should an organization prioritize to effectively verify the security of its AI models?
A. Developing a testing strategy including AI-specific threat modeling and adversarial attack simulations
B. Using standard penetration testing methods
C. Automating vulnerability identification
D. Testing team competencies in IT threat mitigation
Answer: A
Explanation:
The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems.
Effective AI security testing requires:
* AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)
* Adversarial attack simulations (white-box, black-box, gradient-based attacks)
* Evaluation of robustness and manipulation resistance
Option B captures these requirements precisely.
Options A, C, and D do not address AI-specific attack vectors.
References: AAISM Study Guide - AI Security Testing and Adversarial Evaluation.
NEW QUESTION # 108
Which of the following BEST addresses risk associated with hallucinations in AI systems?
A. Automated output validation
B. Human oversight
C. Recursive chunking
D. Content enrichment
Answer: B
Explanation:
AAISM prescribes human-in-the-loop (HITL) controls as the primary safeguard for high-impact generative AI use cases to mitigate hallucination risk. Human oversight ensures critical outputs are reviewed, corrected, and approved before use, with accountability, escalation, and documented decision trails. Automated validators and enrichment help reduce errors but are secondary; recursive chunking is a prompting tactic, not a governance control.
References: AI Security Management™ (AAISM) Body of Knowledge: Responsible AI & Human Oversight; Generative AI Risk Controls-Approval Workflows and Human Review; AAISM Study Guide: Hallucination Risk Treatment with HITL and Approval Gates.
NEW QUESTION # 109
What is the PRIMARY purpose of a dedicated AI management system policy?
A. Complying with external regulations
B. Providing a framework to set AI objectives
C. Minimizing environmental impact
D. Optimizing AI model accuracy
Answer: B
Explanation:
AAISM states that an AI management system policy provides organizational structure by:
* defining AI objectives
* aligning governance
* outlining accountability
* defining roles, responsibilities, and guiding principles
Regulatory compliance (C) is a part of governance but not the overall purpose. Accuracy (B) and environmental impact (A) are narrower focus areas.
References: AAISM Study Guide - AI Management System Policies; Governance Framework Requirements.
NEW QUESTION # 110
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