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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Exam Duration:150 minutes
Certificate Validity Period:3 years
Related Certifications:CISSP
CISM
Passing Score:450 (out of 800)
Exam Price:US$459 (members), US$599 (non-members)
Real Exam Qty:90
Available Languages:English
Exam Format:Scenario-based, Multiple-choice
Recommended Training:ISACA Official AAISM Training
Exam Registration:ISACA AAISM Registration
Sample Questions:ISACA AAISM Sample Questions
Exam Way:Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition: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 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 2
  • 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 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 (Q123-Q128):

NEW QUESTION # 123
Which of the following information is MOST important to include in a centralized AI inventory?

Answer: D

Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.


NEW QUESTION # 124
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?

Answer: A

Explanation:
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 & Responsibilities; Risk Intake and Triage for Privacy/Inference Risks.AAISM Study Guide: Ethics & Responsible AI Escalation Pathways; Governance Board Procedures; Documentation and Decision Records.


NEW QUESTION # 125
Which of the following is the MOST effective way to prevent a model inversion attack?

Answer: A

Explanation:
AAISM identifies differential privacy as the primary mitigation technique against model inversion attacks, which attempt to reconstruct sensitive training data by probing model outputs.


NEW QUESTION # 126
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?

Answer: A

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 # 127
Which of the following is the BEST reason to immediately disable an AI system?

Answer: C

Explanation:
According to AAISM lifecycle management guidance, the best justification for disabling an AI system immediately is the detection of excessive model drift. Drift results in outputs that are no longer reliable, accurate, or aligned with intended purpose, creating significant risks. Performance slowness and overly detailed outputs are operational inefficiencies but not critical shutdown triggers.
Insufficient training should be addressed before deployment rather than after. The trigger for immediate deactivation in production is excessive drift compromising reliability.


NEW QUESTION # 128
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