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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 (Q431-Q436):

NEW QUESTION # 431
A large financial institution is integrating a third-party AI solution into its fraud detection system. Which is the BEST way to reduce AI vendor/supply chain risk?

Answer: C

Explanation:
AAISM emphasizes contractual governance controls as the strongest mechanism for managing AI vendor and supply chain dependencies. Contracts must require:
* secure development lifecycle practices
* documentation of controls
* security testing
* supply chain transparency
Isolated environments (C) help, but they do not ensure upstream integrity. Performance testing (D) is unrelated to security. Annual assessments (A) occur too late to mitigate onboarding risks.
References: AAISM Study Guide - AI Vendor Governance; Contractual Risk Controls.


NEW QUESTION # 432
Within an incident handling process, which of the following would BEST help restore end user trust with an AI system?

Answer: D

Explanation:
Restoring end user trust during incident handling requires visible, immediate assurance that system outcomes are safe and appropriate. AAISM prescribes human oversight and approval gates for high-risk AI decisions, with human validation of outputs before use as a primary control to maintain trust while technical remediation is underway. Prioritization (A) and monitoring (B) aid operations but do not directly rebuild user confidence in outcomes. Post-incident improvements (D) are essential for long-term assurance but do not provide the immediate trust restoration that supervised, human-validated outputs deliver.
References: AI Security Management™ (AAISM) Body of Knowledge - Incident Handling & Communications; Human Oversight and Approval Gates; Trust Restoration During AI Incidents.


NEW QUESTION # 433
Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?

Answer: D

Explanation:
A confusion matrix explicitly tabulates TP, TN, FP, FN, serving as the basis for derived metrics (precision, recall, F1, specificity) and error analysis. Precision and recall are single metrics derived from the matrix; hyperparameter tuning is a process, not an analysis artifact.
References: AAISM Body of Knowledge: Model Evaluation & Assurance-Classification Metrics and Error Analysis; AAISM Study Guide: Confusion Matrix Fundamentals and Derived Measures.


NEW QUESTION # 434
A SaaS-based LLM system has risks including prompt injection, data poisoning, and model exfiltration. What is the BEST way to ensure consistent risk treatment?

Answer: B

Explanation:
AAISM highlights the importance of an AI threat-control matrix that:
- maps threats (e.g., poisoning, exfiltration, injection)
- identifies required controls
- defines assurance/testing activities
- ensures consistent coverage across all AI components


NEW QUESTION # 435
A financial organization is concerned about the risk of prompt injection attacks on its customer service chatbot. Which of the following controls BEST addresses this concern?

Answer: A

Explanation:
AAISM emphasizes preventive technical controls for LLM threats such as prompt injection, including input validation/sanitization, instruction isolation, allow/deny lists, context segmentation, and output filtering.
These reduce the model's exposure to adversarial instructions embedded in user prompts or retrieved context.
Monitoring (A) is detective, not preventive; increasing parameters (B) does not inherently improve security against injection; human-in-the-loop (D) is valuable for high-risk decisions but does not directly neutralize injection vectors at the control boundary the way input validation and content filtering do.
References: AI Security Management (AAISM) Body of Knowledge - Technical Controls for LLM Security; Input/Output Filtering and Context Isolation; Secure Inference and Prompt Injection Mitigations.


NEW QUESTION # 436
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