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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 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 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 (Q277-Q282):

NEW QUESTION # 277
Which phase of the AI data life cycle presents the GREATEST inherent risk?

Answer: A

Explanation:
AAISM identifies training as the phase with the highest inherent risk because this is where:
* data poisoning can occur
* sensitive data may be exposed
* bias can be introduced
* model inversion risks originate
* security and privacy vulnerabilities are embedded
Preparation (C) carries risk but is less critical. Maintenance (B) and monitoring (A) involve operational safeguards, not foundational risk creation.
References: AAISM Study Guide - AI Data Life Cycle Risks; High-Risk Model Training Phase.


NEW QUESTION # 278
A regulator warns of increased risk of AI re-identification attacks on anonymized datasets. What should the information security manager do FIRST?

Answer: D

Explanation:
AAISM states that anonymization is not permanent and may be reversible through re-identification attacks.
The first action should be to evaluate and measure the actual privacy risk through:
* adversarial re-identification testing
* privacy audits
* monitoring for misuse
This provides the factual basis needed before making destructive or operational decisions.
Access control (D) is important but not the FIRST step. Deleting datasets (B) is premature. Assuming anonymization is permanent (A) violates AI privacy principles.
References: AAISM Study Guide - Privacy Risk, Re-Identification Threats, Adversarial Testing.


NEW QUESTION # 279
When implementing a generative AI system, which of the following approaches will BEST prevent misalignment between the corporate risk appetite and tolerance?

Answer: C

Explanation:
AAISM governance guidance specifies that alignment between AI system adoption and organizational risk appetite is achieved by defining and monitoring acceptable levels of system risk. This ensures that generative AI operations remain within boundaries approved by leadership and compliance frameworks. While KPIs track performance, they do not ensure alignment with risk tolerance. AI impact assessments help identify risks but do not maintain continuous oversight. A risk register records risks but does not dynamically enforce acceptable thresholds. The most effective governance approach is to establish and monitor acceptable AI system risk levels.
References:
AAISM Study Guide - AI Governance and Program Management (Risk Appetite and Tolerance Alignment) ISACA AI Security Management - Generative AI Risk Governance


NEW QUESTION # 280
An organization plans to implement a new AI system. Which of the following is the MOST important factor in determining the level of risk monitoring activities required?

Answer: A

Explanation:
AAISM risk management guidance clarifies that the organization's risk tolerance is the most important factor in determining how much monitoring is needed. Risk tolerance specifies the amount of risk the organization is willing to accept and defines the threshold for triggering monitoring or mitigation activities. Risk appetite is broader and strategic, while tolerance sets the operational limits. The number of users may influence scale, and compensating controls may affect resilience, but neither dictates monitoring intensity as directly as risk tolerance.
References:
AAISM Study Guide - AI Risk Management (Risk Appetite vs. Tolerance)
ISACA AI Security Management - Monitoring Based on Risk Tolerance


NEW QUESTION # 281
Which of the following is MOST important to consider when validating a third-party AI tool?

Answer: A

Explanation:
The AAISM framework specifies that when adopting third-party AI tools, the right to audit is the most critical contractual and governance safeguard. This ensures that the organization can independently verify compliance with security, privacy, and ethical requirements throughout the lifecycle of the tool. Terms and conditions provide general usage guidance but often limit liability rather than ensuring transparency. Industry certifications may indicate good practice but do not substitute for direct verification. Roundtable testing is useful for evaluation but lacks enforceability. Only the contractual right to audit provides formal assurance that the tool operates in accordance with organizational policies and external regulations.


NEW QUESTION # 282
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