ISACA AAISM Exam Questions - Choice Of Certified Professionals [2026]

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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 (Q271-Q276):

NEW QUESTION # 271
To facilitate the adoption of a new AI system, an organization has provided data access to third- party solutions. Which of the following BEST mitigates risk associated with third-party software?

Answer: A

Explanation:
Conducting regular external audits provides independent assessment and verification that third- party solutions are operating securely, complying with requirements, and properly handling organizational data. This is the most effective way to mitigate ongoing risks associated with third- party software access and AI integration.


NEW QUESTION # 272
Which of the following should be the PRIMARY consideration for an organization concerned about liabilities associated with unforeseen behavior from agentic AI systems?

Answer: B

Explanation:
AAISM governance guidance stresses that when dealing with agentic AI systems capable of autonomous decision-making, the primary consideration is accountability. Without clear accountability structures, unforeseen or harmful outcomes may result in unmitigated liability for the organization. While dependencies, base models, and defined risk levels are important, they do not directly address who is responsible when systems act unpredictably. The key governance safeguard is the implementation of an accountability model that ensures liability and oversight are properly assigned.


NEW QUESTION # 273
When designing an AI security architecture, what is the PRIMARY purpose of using adversarial training?

Answer: D


NEW QUESTION # 274
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 is explicitly defined in AAISM as the framework used to interpret classification performance by listing:
* true positives
* true negatives
* false positives
* false negatives
Precision (B) and recall (D) are derived metrics that use parts of the matrix but do not show the full picture.
Hyperparameter tuning (A) is unrelated.
References: AAISM Study Guide - AI Model Evaluation Metrics; Confusion Matrix.


NEW QUESTION # 275
Which of the following involves documenting and monitoring the complete journey of data as it flows through an AI system?

Answer: B

Explanation:
Data lineage records and monitors the end-to-end journey of data--sources, movements, transformations, storage locations, uses, and dependencies--providing traceability, auditability, and accountability across the AI lifecycle. "Origin" is a single point (provenance), "transformation" is one step within the flow, and "processing" is a general activity rather than a governance record of the entire path.


NEW QUESTION # 276
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