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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Exam Format:	Multiple-choice, Scenario-based
Passing Score:	450 (out of 800)
Real Exam Qty:	90
Related Certifications:	CISM CISSP
Exam Duration:	150 minutes
Exam Price:	US$459 (members), US$599 (non-members)
Available Languages:	English
Certificate Validity Period:	3 years
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:

Topic	Details
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 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 3	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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q224-Q229):

NEW QUESTION # 224
Which of the following is the BEST reason to include clear AI usage restrictions in an organization's acceptable use policy?

A. To prevent model drift by restricting personal use of training datasets
B. To improve inference speed by controlling which APIs can be accessed
C. To reduce storage costs by limiting employee use of large models
D. To ensure employees do not expose sensitive data to external AI tools

Answer: D

Explanation:
Clear usage restrictions help prevent employees from inputting or sharing sensitive information with external AI tools, reducing the risk of data leakage and protecting confidentiality.

NEW QUESTION # 225
Which of the following is MOST important for effective AI risk management?

A. Utilization of best practice AI risk management frameworks
B. Internal stakeholder participation in AI risk management processes
C. Risk measurement during an early stage of the AI system life cycle
D. Creation of separate risk management processes for AI-specific risk

Answer: B

Explanation:
AAISM emphasizes that effective AI risk management is socio-technical and hinges on cross-functional stakeholder participation (business owners, security, privacy, legal, compliance, data science, operations).
Participation ensures risks are identified, prioritized, and treated in alignment with business context and risk appetite. While early measurement (B) and using frameworks (D) are important, and separate processes (A) can create silos, stakeholder participation is the primary success factor ensuring governance is practical, adopted, and continuously improved.
References: AI Security Management (AAISM) Body of Knowledge - Governance Roles & Accountability; Integrated Risk Management for AI; Cross-Functional Risk Committees and RACI for AI Programs.

NEW QUESTION # 226
What is the PRIMARY purpose of a dedicated AI management system policy?

A. Providing a framework to set AI objectives
B. Optimizing AI model accuracy
C. Minimizing environmental impact
D. Complying with external regulations

Answer: A

Explanation:
AAISM states that an AI management system policy provides organizational structure by:
- defining AI objectives
- aligning governance
- outlining accountability
- defining roles, responsibilities, and guiding principles

NEW QUESTION # 227
An organization is reviewing an AI application to determine whether it is still needed. Engineers have been asked to analyze the number of incorrect predictions against the total number of predictions made. Which of the following is this an example of?

A. Explainable decision-making
B. Model validation
C. Control self-assessment (CSA)
D. Key performance indicator (KPI)

Answer: D

Explanation:
AAISM guidance identifies metrics like error rate versus total predictions as a key performance indicator (KPI) for evaluating AI model effectiveness. KPIs provide measurable values to assess performance against objectives. Model validation is broader and occurs prior to production use, testing the model against predefined standards. Control self-assessment relates to governance processes, not predictive accuracy.
Explainable decision-making refers to interpretability, not error-rate evaluation. Thus, analyzing incorrect predictions against total predictions is a performance measure, making it a KPI.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Performance Metrics and KPIs) AI Security Management Study Guide - Accuracy and Error Metrics

NEW QUESTION # 228
Which of the following controls BEST mitigates the risk of data poisoning?

A. Intrusion detection
B. Digital watermarking
C. Data validation
D. Data set restoration

Answer: C

Explanation:
The AAISM technical controls framework emphasizes data validation as the primary safeguard against data poisoning attacks. Poisoning occurs when attackers insert malicious or corrupted data into training sets. Validation techniques verify the quality, authenticity, and consistency of input data before training, preventing compromised samples from corrupting the model.
Restoration helps after compromise, watermarking protects ownership, and intrusion detection monitors networks rather than data quality. The most effective preventive measure is data validation.

NEW QUESTION # 229
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