Exam AAISM Questions & AAISM Paper

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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Certificate Validity Period:3 years
Available Languages:Spanish, English
Passing Score:450/800
Exam Price:USD $399
Exam Format:Scenario-based Questions, Multiple Choice
Real Exam Qty:90
Exam Duration:150 minutes
Related Certifications:ISACA Advanced in AI Security Management (AAISM)
Sample Questions:ISACA AAISM Sample Questions
Exam Way:Online remote proctored exam or test center exam
Pre Condition:Candidates must hold an active CISM or CISSP certification.
Official Syllabus URL:https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
Topic 3
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which of the following is a key risk indicator (KRI) for an AI system used for threat detection?

Answer: C

Explanation:
AAISM materials emphasize that in operational AI systems, key risk indicators (KRIs) must reflect risks to performance and reliability rather than technical design factors alone. In the case of threat detection, the most relevant KRI is the frequency of system overrides by human analysts, as this indicates a lack of trust, frequent false positives, or poor detection accuracy. Training epochs, model depth, and training time are technical metrics but do not directly measure operational risk. Analyst overrides represent a practical measure of system effectiveness and risk.
References:
AAISM Study Guide - AI Risk Management (Operational KRIs for AI Systems) ISACA AI Security Management - Monitoring AI Effectiveness


NEW QUESTION # 31
In managing the life cycle of agentic AI systems, which of the following would be the GREATEST concern during the destruction phase of AI models and associated data?

Answer: A

Explanation:
The greatest concern during the destruction phase is the inability to verify that AI models and associated data were securely and completely eliminated. Without verifiable audit trails, residual models or sensitive data may remain accessible, creating significant security, privacy, and compliance risks.


NEW QUESTION # 32
After implementing generative AI tools, an organization was notified of new critical compliance requirements aimed at verifying how AI is being used. Which of the following should be done FIRST?

Answer: C

Explanation:
Establishing a responsible use policy provides the foundational governance framework that defines how AI should be used in alignment with compliance requirements, enabling subsequent controls, reviews, and reporting to be properly structured.


NEW QUESTION # 33
Which of the following BEST enables an organization to strengthen information security controls around the use of generative AI applications?

Answer: B

Explanation:
For generative AI, the primary enterprise security exposure is data and content exfiltration or policy violations at output, including leakage of sensitive data, toxic content, or regulatory non-compliance.
AAISM prescribes policy-aligned output monitoring (e.g., DLP checks, PII/PHI detection, toxicity/safety filters, watermark/attribution checks) integrated into inference gateways to enforce organizational policies and evidence compliance. Exceeding benchmarks (A) is not a control; training-data validation (C) may be infeasible with third-party LLMs; and kill switches (D) are essential contingency controls but do not continuously strengthen everyday security posture.
References: AI Security Management (AAISM) Body of Knowledge - GenAI Governance and Guardrails; Output Filtering and DLP Controls; Policy Enforcement at Inference. AAISM Study Guide - Monitoring & Auditing of GenAI; Gateway Patterns for Safe Use; Control Effectiveness Measures.


NEW QUESTION # 34
What is the PRIMARY purpose of a dedicated AI management system policy?

Answer: D

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


NEW QUESTION # 35
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