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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q293-Q298):

NEW QUESTION # 293
Which of the following should be the PRIMARY consideration for an organization concerned about liabilities associated with unforeseen behavior from agentic AI systems?

Answer: D

Explanation:
AAISM governance guidance stresses that when dealing with agentic AI systems capable of autonomous decision-making, the primary consideration is accountability. Without clear accountability structures, unforeseen or harmful outcomes may result in unmitigated liability for the organization. While dependencies, base models, and defined risk levels are important, they do not directly address who is responsible when systems act unpredictably. The key governance safeguard is the implementation of an accountability model that ensures liability and oversight are properly assigned.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Accountability and Liability Management) AI Security Management Study Guide - Responsible Oversight of Agentic AI


NEW QUESTION # 294
Which of the following AI data life cycle phases presents the GREATEST inherent risk?

Answer: C

Explanation:
The data Preparation phase-covering sourcing, collection, labeling, cleansing, and provenance-presents the greatest inherent risk because it is where privacy, consent, representativeness, bias, quality, lineage, and legality must be established. Decisions and defects here propagate into training and downstream use, amplifying ethical, regulatory, and accuracy risks.
While Training can introduce additional risks (e.g., poisoning, leakage), these are frequently mitigated by controls that depend on having trustworthy prepared data. Monitoring and Maintenance are later-life-cycle phases oriented toward detection and correction; they are critical but inherently rely on the foundation set during Preparation.
References: AI Security Management (AAISM) Body of Knowledge: "AI Data Lifecycle Risks-Sourcing, Consent, Provenance, and Bias," "Risk Treatment Priorities Across the Lifecycle"; AAISM Study Guide:
"Data Preparation Controls and Quality Gates," "Bias and Privacy Risk Controls at Ingestion."


NEW QUESTION # 295
Which of the following is the PRIMARY benefit of having robust model versioning and rollback capabilities for AI models?

Answer: C

Explanation:
Robust model versioning and rollback capabilities allow organizations to quickly restore a previously stable and validated model if a new version introduces degraded performance, instability, security issues, or operational failures. This directly mitigates the risk associated with model degradation or failure in production.


NEW QUESTION # 296
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: 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 # 297
Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?

Answer: C

Explanation:
A confusion matrix is explicitly defined in AAISM as the framework used to interpret classification performance by listing:
- true positives
- true negatives
- false positives
- false negatives


NEW QUESTION # 298
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