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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 Duration:	150 minutes
Exam Price:	USD $399
Passing Score:	450/800
Exam Format:	Multiple Choice, Scenario-based Questions
Available Languages:	English, Spanish
Real Exam Qty:	90
Certificate Validity Period:	3 years
Related Certifications:	ISACA Advanced in AI Security Management (AAISM)
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Online remote proctored exam or test center exam
Pre Condition:	Candidates must 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 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q239-Q244):

NEW QUESTION # 239
Which of the following BEST describes the role of model cards in AI solutions?

A. They are used to automatically fine-tune AI models by adjusting hyperparameters based on user feedback
B. They are primarily used to visualize the performance of AI models
C. They provide a standardized way to document the training data and AI model use cases
D. They help developers create synthetic data and train AI models

Answer: C

Explanation:
AAISM positions model cards as standardized documentation artifacts that record intended use and out-of-scope use, training/evaluation data characteristics, performance metrics across groups, limitations/risks, and governance controls/owners. Their purpose is transparency and assurance, not automated tuning or synthetic data generation. Visualization (A) may appear within a card, but the core role is structured documentation for governance, risk, and compliance.

NEW QUESTION # 240
An organization is implementing an AI-based credit assessment engine using internal and third-party customer data. Which of the following BEST aligns with data management controls for the AI life cycle?

A. Encrypted isolation and dynamic access controls on training data pipelines
B. Use of hashed identifiers to anonymize datasets used for model validation and internal analytics
C. Documented procedures for data sourcing, lineage tracking, and quality validation
D. Limitation of model training to structured data from vetted sources to minimize ingestion risk

Answer: C

Explanation:
The strongest alignment with AI life-cycle data management controls is establishing documented procedures for data sourcing, lineage tracking, and quality validation. Governance requires end-to-end data controls- provenance, consent, sourcing approvals, lineage, quality checks, labeling standards, retention, and auditability-so models are trained and validated on trustworthy, compliant data. Hashing alone is pseudo- anonymization and does not deliver full governance. Restricting to structured data is an implementation constraint, not a control set. Encryption and access control protect confidentiality/availability but do not ensure lineage or quality.
References: AI Security Management (AAISM) Body of Knowledge: Data Governance & Stewardship; AI Data Lifecycle Controls; Data Provenance, Lineage, and Quality Assurance. AAISM Study Guide: Data Intake Procedures; Lineage and Audit Evidence; Quality Gates and Approval Workflows.

NEW QUESTION # 241
Which BEST describes the role of model cards in AI solutions?

A. They visualize AI model performance
B. They help developers create synthetic data
C. They document training data and AI model use cases
D. They automatically fine-tune AI models

Answer: C

Explanation:
AAISM explains that model cards provide structured documentation about AI models, including:
- intended use cases
- training data characteristics
- ethical considerations
- known limitations
- risk factors
- performance benchmarks

NEW QUESTION # 242
Which of the following reviews MUST be conducted as part of an AI impact assessment?

A. Identification of environmental and societal consequences
B. Evaluation of model reproducibility
C. Testing, evaluation, validation, and verification
D. Security control self-assessment (CSA)

Answer: A

Explanation:
The AAISM material frames an AI impact assessment as broader than a technical model review. It emphasizes that governance of AI includes assessing societal, environmental, and human-rights impacts in addition to organizational risk. The official guidance notes that high-level impact assessments must explicitly consider "potential consequences for individuals, communities, and the environment" as part of responsible AI governance. Activities such as testing/validation (A) and reproducibility (B) are critical in the development lifecycle but are not, by themselves, sufficient to constitute an impact assessment. A CSA (C) is focused on security controls, which is narrower than overall impact. The mandatory element that differentiates an AI impact assessment is the assessment of environmental and societal consequences, making option D the correct answer in alignment with governance expectations.
References: AI Security Management™ (AAISM) Study Guide - AI Governance and Impact Assessments; Responsible and Trustworthy AI Section.

NEW QUESTION # 243
What is the GREATEST benefit of performing AI security risk assessments?

A. Enabling risk prioritization
B. Securing appropriate funding
C. Updating the risk register
D. Implementing privacy controls

Answer: A

Explanation:
AAISM emphasizes that the primary value of AI security risk assessments is prioritizing risks based on likelihood, impact, and business relevance.

NEW QUESTION # 244
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