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Our ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) PDF format is user-friendly and accessible on any smart device, allowing applicants to study from anywhere at any time. We have included actual and updated ISACA AAISM questions in this ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) Dumps PDF file. Our ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) exam dumps PDF format is designed to help individuals acquire the knowledge necessary to succeed in the test.

ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Related Certifications:ISACA Advanced in AI Security Management (AAISM)
Certificate Validity Period:3 years
Exam Duration:150 minutes
Exam Price:USD $399
Exam Format:Scenario-based Questions, Multiple Choice
Real Exam Qty:90
Available Languages:Spanish, English
Passing Score:450/800
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:

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 (Q122-Q127):

NEW QUESTION # 122
Which of the following information is MOST important to include in a centralized AI inventory?

Answer: B

Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.


NEW QUESTION # 123
Which of the following is MOST important to monitor in order to ensure the effectiveness of an organization's AI vendor management program?

Answer: D

Explanation:
The AAISM framework specifies that the primary metric of effectiveness in vendor management is the vendor's compliance with AI-related requirements defined in contracts and governance frameworks. This provides measurable assurance that vendors adhere to agreed-upon privacy, security, and ethical standards. Reviews of threat reports, training results, or research participation are supplemental and may support continuous improvement, but they do not establish compliance accountability. Governance requires a direct focus on whether contractual and regulatory obligations are being fulfilled. Therefore, vendor compliance with AI requirements is the most important monitoring focus.


NEW QUESTION # 124
An organization concerned about the ethical and responsible use of a newly developed AI product should consider implementing:

Answer: C

Explanation:
The AAISM framework highlights that organizations adopting AI must ensure accountability structures are in place to govern ethical and responsible use. An accountability model assigns clear responsibility for decisions, outputs, and risks related to AI systems. While model cards provide transparency about a model's design and performance, they are primarily documentation tools. Vendor monitoring focuses on third-party oversight, not internal accountability. Security by design improves resilience but does not by itself address ethical use. The governance approach that most directly supports responsible and ethical AI deployment is an accountability model.
References:
AAISM Study Guide - AI Governance and Program Management (Ethical AI and Accountability) ISACA AI Security Management - Responsible AI Practices


NEW QUESTION # 125
An organization is adopting an agentic AI solution from an external vendor to support its internal IT operations. To evaluate the security posture of this system, which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?

Answer: D

Explanation:
Third-party audit reports provide independent assurance that the vendor's stated controls are designed and operating effectively against recognized criteria. Such attestations (e.g., audit/assurance frameworks) are traceable, repeatable, and verifiable, and they support supply-chain risk reviews and contractual assurance.
Internal red-team reports are not independent, industry "peer reviews" are not control attestations, and whitepapers are marketing/educational materials without evidence of control operation.
References: AI Security Management (AAISM) Body of Knowledge: Third-Party & Supply-Chain Assurance; Independent Assessment and Audit Evidence; Vendor Risk Management. AAISM Study Guide:
Assurance Artifacts for AI Services; Evaluating External AI Providers.


NEW QUESTION # 126
Which of the following is the BEST mitigation technique for a model inversion attack?

Answer: C

Explanation:
Privacy-preserving methods such as differential privacy limit the amount of information a model can reveal about its training data, directly reducing the risk of reconstructing sensitive inputs through model inversion attacks.


NEW QUESTION # 127
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