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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 (Q348-Q353):

NEW QUESTION # 348
A medical imaging AI model is vulnerable to adversarial information that could cause misdiagnosis. Which control would BEST help detect such manipulations during inference?

Answer: A

Explanation:
Anomaly detection during inference monitors inputs and outputs for unusual patterns that may indicate adversarial manipulation, enabling real-time detection of inputs designed to mislead the model.


NEW QUESTION # 349
Which of the following approaches BEST helps reduce model bias?

Answer: A

Explanation:
AAISM states that the strongest control against bias is ensuring representative, diverse, and inclusive training data. This directly minimizes skew and systemic underrepresentation.
Complex architectures (B) do not reduce bias and may exacerbate it. Reducing updates (C) increases drift.
More labels (D) improves supervision quality but does not inherently solve bias if the data itself is skewed.
References: AAISM Study Guide - Bias Mitigation and Data Quality Requirements.


NEW QUESTION # 350
When using AI as part of incident response, which of the following BEST ensures the automation aligns with regulatory and governance obligations?

Answer: C

Explanation:
AAISM prescribes risk-based, human-in-the-loop orchestration for safety-critical or regulated actions. A tiered automation strategy that gates autonomy by incident severity, data sensitivity, and regulatory requirements ensures accountability, auditability, and proportionality, satisfying governance obligations.


NEW QUESTION # 351
Which of the following strategies BEST ensures generative AI tools do not expose company data?

Answer: C

Explanation:
AAISM prioritizes preventive controls at the point of use for generative AI, specifically input-governance and DLP controls that block or redact confidential, regulated, or high-risk data before it can be sent to external models. Audits, pre-deployment tests, and regulatory conformance are necessary but do not themselves prevent an employee from pasting sensitive content into prompts. Enforcing input restrictions, pattern-based redaction, policy-aware controls, and allow-lists for approved contexts provides the highest assurance of preventing exposure.
References:* AI Security Management (AAISM) Body of Knowledge: Data loss prevention for AI; prompt
/input controls; approved channels and guardrails for generative AI.* AI Security Management Study Guide: Preventive over detective controls for confidentiality; enterprise guardrails at prompt capture and egress points.


NEW QUESTION # 352
Which of the following is BEST to review in order to detect issues related to data used throughout an AI system's life cycle?

Answer: D

Explanation:
Data flow diagrams provide a comprehensive view of how data moves through the system across all stages of the AI life cycle, enabling identification of issues related to data handling, transformation, and usage.


NEW QUESTION # 353
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