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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 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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q163-Q168):

NEW QUESTION # 163
Which of the following BEST describes the role of risk documentation in an AI governance program?

Answer: B

Explanation:
In AAISM governance guidance, risk documentation is described as the structured record that defines the organization's risk appetite and tolerance levels for AI initiatives. By outlining acceptable levels of risk, documentation ensures decision-makers can approve, monitor, and adjust AI projects within defined boundaries. While it may also serve audit functions, technical analysis, or communication to stakeholders, its primary role is to formalize risk acceptance thresholds and integrate them into governance and decision- making. This aligns directly with the governance requirement to align AI adoption with organizational risk appetite.
References:
AAISM Study Guide - AI Governance and Program Management (Risk Documentation and Appetite) ISACA AI Security Management - Governance, Risk and Compliance Integration


NEW QUESTION # 164
An attacker triggers several AI agents to execute CPU-heavy tasks simultaneously, exhausting compute quotas. Which of the following agentic AI threats is this an example of?

Answer: D

Explanation:
Unbounded consumption occurs when AI agents are manipulated into excessively consuming computational resources, such as CPU, memory, or API quotas. Triggering multiple CPU- intensive tasks simultaneously to exhaust compute capacity is a direct example of this threat.


NEW QUESTION # 165
When evaluating a third-party AI service provider, which master services agreement (MSA) provision is MOST critical for managing security risk?

Answer: B

Explanation:
AAISM emphasizes strong contractual restrictions on how vendors use customer data, especially prohibiting vendors from using customer inputs to train or fine-tune shared models.
This protects against:
* data leakage
* intellectual property exposure
* regulatory violations
* shadow training of external models
Log sharing (B) and query limits (D) are operational controls but do not directly prevent data misuse.
Unlimited retraining (A) has no relevance to security.
References: AAISM Study Guide - Vendor Risk Management; Data Usage Restrictions in Contracts.


NEW QUESTION # 166
Which of the following is the BEST mitigation control for membership inference attacks on AI systems?

Answer: A

Explanation:
Membership inference attacks attempt to determine whether a particular data point was part of a model's training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.


NEW QUESTION # 167
A large financial services organization wants to use AI to create financial forecasts for clients.
Which of the following implementations is MOST likely to achieve the highest level of security?

Answer: B

Explanation:
A private implementation keeps data, models, and processing within the organization's controlled environment, ensuring stronger protection of sensitive financial information, better compliance with regulatory requirements, and reduced exposure to external threats.


NEW QUESTION # 168
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