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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Available Languages:English
Exam Duration:150 minutes
Passing Score:450 (out of 800)
Certificate Validity Period:3 years
Exam Price:US$459 (members), US$599 (non-members)
Exam Format:Multiple-choice, Scenario-based
Real Exam Qty:90
Related Certifications:CISSP
CISM
Recommended Training:ISACA Official AAISM Training
Exam Registration:ISACA AAISM Registration
Sample Questions:ISACA AAISM Sample Questions
Exam Way:Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition:Hold an active CISM or CISSP certification
Official Syllabus URL:https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q339-Q344):

NEW QUESTION # 339
AI developers often find deep learning systems difficult to explain PRIMARILY because:

Answer: D

Explanation:
AAISM notes that deep learning systems lack transparency due to complex neural architectures, where internal representations are statistical, nonlinear, and not directly interpretable.
While probability (C) and data sourcing (D) contribute to opacity, the root cause is the intrinsic complexity and opacity of deep neural networks.
References: AAISM Study Guide - Explainability Challenges in Deep Learning.


NEW QUESTION # 340
Which of the following is MOST important for effective AI risk management?

Answer: B

Explanation:
AAISM positions early and continuous risk assessment as a critical success factor. The guidance states that AI risk should be "identified, analyzed, and measured starting from the design and concept phases, before significant investment and deployment." This ensures that high-impact risks (e.g., bias, privacy violations, safety issues) can be mitigated or designed-out before they become embedded in production systems.


NEW QUESTION # 341
When evaluating a new AI tool for intrusion prevention, which of the following is the MOST important consideration to ensure the tool fits within the existing program architecture?

Answer: D

Explanation:
The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization' s established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance. While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to-and can be verified against-the control objectives that define the program's intended outcomes.
References:* AI Security Management (AAISM) Body of Knowledge: AI Governance and Program Management - Security program alignment, control objectives, and requirements traceability* AI Security Management Study Guide: Control objective mapping, architecture fit criteria, and solution selection governance


NEW QUESTION # 342
During which phase of the test, evaluation, verification, and validation (TEVV) model does the organization ensure the proposed AI solution to be procured meets business needs as expected?

Answer: A

Explanation:
Validation focuses on confirming that the AI solution fulfills intended business needs and use cases, ensuring it performs as expected in real-world conditions before or during procurement decisions.


NEW QUESTION # 343
The PRIMARY goal of data poisoning attacks is to:

Answer: B

Explanation:
AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine- tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production.


NEW QUESTION # 344
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