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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
Related Certifications:	CISSP CISM
Certificate Validity Period:	3 years
Real Exam Qty:	90
Passing Score:	450 (out of 800)
Exam Format:	Scenario-based, Multiple-choice
Exam Duration:	150 minutes
Exam Price:	US$459 (members), US$599 (non-members)
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 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 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 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 (Q372-Q377):

NEW QUESTION # 372
Which of the following is the MOST important consideration when deciding how to compose an AI red team?

A. Time-to-market constraints
B. Resource availability
C. Compliance requirements
D. AI use cases

Answer: D

Explanation:
AAISM materials specify that the composition of an AI red team must be tailored to the organization's AI use cases. The purpose of red-teaming is to simulate realistic adversarial conditions aligned with the actual applications of AI. For example, testing a generative model requires different expertise than testing a fraud detection system. While resource availability, compliance requirements, and time-to-market pressures are practical considerations, they are secondary to aligning team expertise with use case scenarios. The most important factor is therefore the AI use cases themselves.

NEW QUESTION # 373
Which of the following presents the GREATEST challenge to gaining an understanding of how a deep learning system operates?

A. Steep learning curve for interpretation of system testing results
B. Ineffectiveness of traditional data flow mapping approaches
C. Insufficient output volumes and diversity to draw definitive conclusions
D. Complex non-linear relationships among features and parameters

Answer: D

Explanation:
Deep learning systems rely on highly complex non-linear relationships among large numbers of features, hidden layers, and parameters. These interactions make it difficult to understand, interpret, and explain how the model reaches specific decisions or outputs.

NEW QUESTION # 374
A financial organization is concerned about AI data poisoning. Which control BEST mitigates this risk?

A. Implementing a break-glass policy
B. Using training data from multiple sources
C. Delivering AI-specific security awareness training
D. Transparency with customers about data sources

Answer: B

Explanation:
AAISM outlines that diversifying training data sources reduces the likelihood and impact of poisoning because:
- attack samples become harder to inject
- anomalies are easier to detect
- corrupted data has reduced influence on model behavior

NEW QUESTION # 375
A preliminary risk assessment of a SaaS-based large language model (LLM) business support system has identified prompt injection, data poisoning, and model exfiltration as material threats.
Which of the following is the BEST approach to ensure risks are treated consistently?

A. Implementing an AI threat control matrix that maps threats to specific controls and assurance activities
B. Applying control baselines from a recognized industry standard to AI components
C. Focusing resources on post-deployment red teaming and deferring control selection until post go- live feedback is received
D. Relying on vendor independent audit reports and service level agreements (SLAs) as evidence of AI risk coverage

Answer: A

Explanation:
AAISM prescribes building and maintaining an AI Threat-Control Matrix to ensure consistent, repeatable risk treatment. The matrix traces each material threat (e.g., prompt injection, poisoning, exfiltration) to named controls, test/evidence procedures, and assurance owners across the lifecycle. Baselines and vendor attestations can support assurance but are insufficient alone; deferring control selection until after deployment conflicts with AAISM's proactive treatment principle.

NEW QUESTION # 376
A large organization has appointed the CISO to form an AI steering committee. Which of the following should be the CISO's FIRST course of action?

A. Performing a gap analysis
B. Selecting an AI risk framework
C. Benchmarking against peer organizations
D. Identifying the types of AI in use

Answer: D

Explanation:
The first step in establishing effective AI governance is identifying the types of AI currently in use across the organization. Understanding existing AI systems, use cases, and exposure is necessary before selecting frameworks, benchmarking, or conducting meaningful gap analyses.

NEW QUESTION # 377
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