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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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q427-Q432):

NEW QUESTION # 427
Which of the following is the MOST important consideration when an organization is adopting generative AI for personalized advertising?

Answer: D

Explanation:
In AAISM, usage of AI for activities involving personal data and profiling, such as personalized advertising, is explicitly mapped to stringent regulatory and compliance requirements (e.g., data protection, consent, profiling limitations, fairness obligations). The material notes that these activities may trigger "heightened regulatory scrutiny, mandatory impact assessments, and potential penalties for non-compliance."


NEW QUESTION # 428
Which of the following is the MOST important course of action to ensure an AI system produces ethical results?

Answer: D

Explanation:
Embedding responsible practices throughout the AI life cycle ensures that ethical considerations are continuously addressed during design, development, deployment, operation, and retirement.
This approach systematically promotes fairness, accountability, transparency, privacy, and risk management across the entire AI system lifecycle.


NEW QUESTION # 429
Which of the following is the GREATEST concern when a vendor enables generative AI features for an organization's critical system?

Answer: B

Explanation:
When enabling genAI capabilities in a critical system, AAISM prioritizes controlling access to the model and its interfaces (prompt surfaces, context windows, tools/functions, and connected data) because exposure expands the attack surface for prompt injection, data exfiltration, jailbreaks, and misuse. Monitoring (C) is necessary but detective; ethics and bias (D) are vital but secondary to immediate safety and security of a mission-critical environment; proposed regulations (B) are not an immediate operational risk.
References: AAISM Body of Knowledge: GenAI Security-Access Governance, Interface Hardening, and Prompt Surface Controls; AAISM Study Guide: Critical System Safeguards-Least Privilege, Guardrails, and Abuse Prevention.


NEW QUESTION # 430
An organization needs large data sets to perform application testing. Which of the following would BEST fulfill this need?

Answer: C

Explanation:
According to AAISM study guidance, the most direct and effective way to obtain large volumes of diverse data for application testing is through open-source data repositories. These repositories provide freely available, well-documented, and often standardized data that supports testing and benchmarking in a compliant manner. Model cards document AI behavior but do not provide data. Incorporating search content may introduce legal, privacy, and quality risks. Data augmentation is useful for expanding existing sets but does not provide the breadth or size required when starting with insufficient data. The recommended best practice for sourcing large testing datasets is therefore the use of open-source repositories.
References:
AAISM Study Guide - AI Technologies and Controls (Data Sources and Testing Practices) ISACA AI Security Management - Data Governance and Compliance in AI Testing


NEW QUESTION # 431
An automotive manufacturer uses AI-enabled sensors on machinery to monitor variables such as vibration, temperature, and pressure. Which of the following BEST demonstrates how this approach contributes to operational resilience?

Answer: D

Explanation:
AAISM highlights that AI-enabled predictive maintenance improves operational resilience by using real-time sensor monitoring to schedule repairs based on actual conditions rather than fixed schedules. This prevents unexpected breakdowns, reduces downtime, and ensures continuity of operations. Regular maintenance based on recommendations is static and may not reflect real conditions. Manual reviews are slow and inefficient. Full automation of repairs without human oversight is not realistic or safe in critical manufacturing. The approach that best demonstrates resilience is real-time condition-based repair scheduling.


NEW QUESTION # 432
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