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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 Price:USD $399
Passing Score:450/800
Exam Duration:150 minutes
Available Languages:English, Spanish
Related Certifications:ISACA Advanced in AI Security Management (AAISM)
Certificate Validity Period:3 years
Exam Format:Scenario-based Questions, Multiple Choice
Real Exam Qty:90
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 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 (Q37-Q42):

NEW QUESTION # 37
A school district contracts a third-party provider for AI-based curriculum recommendations. Which of the following is the BEST way to ensure the vendor uses AI responsibly?

Answer: A

Explanation:
AAISM emphasizes transparency artifacts from vendors to enable due diligence and assurance.
A model card documents intended use, data sources, limitations, performance across subgroups, known risks, and evaluation procedures--information necessary to assess safety, fairness, and compliance for sensitive contexts like education. SSO and support are useful operational features; generic ToS updates are insufficient without model-specific disclosures.


NEW QUESTION # 38
Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models leveraging fraud detection systems?

Answer: C

Explanation:
Differential privacy aims to protect the privacy of any single individual's data contribution while still enabling useful aggregate learning and statistical analysis. Noise mechanisms are calibrated so that results remain informative for modeling (e.g., fraud patterns) without revealing whether any particular person's data was included or enabling inference about them. Accuracy, speed, and compute efficiency can be secondary considerations, but the primary objective is privacy protection with utility preserved.
References: AI Security Management (AAISM) Body of Knowledge: Privacy-Preserving ML; Differential Privacy Objectives and Mechanisms. AAISM Study Guide: Individual Contribution Protection; Utility- Privacy Trade-offs and Calibration in Applied Models.


NEW QUESTION # 39
Which of the following is the MAIN objective of the operational phase of AI life cycle management?

Answer: C

Explanation:
In the operational phase, AAISM emphasizes continuous monitoring of models for performance, stability, robustness, drift, data quality, security events, and policy compliance. This includes telemetry, thresholds, alerts, incident response for AI failures, and evidence collection for audits.
Alignment to business needs is established earlier in planning/governance; algorithmic optimization and feedback collection are supporting activities, but the primary operational objective is live monitoring and assurance to keep risk within tolerance.


NEW QUESTION # 40
Personal data used to train AI systems can BEST be protected by:

Answer: D

Explanation:
AAISM guidance on privacy-preserving AI highlights anonymization as the most effective means of protecting personal data used in training. By irreversibly removing or masking identifiable attributes, anonymization ensures that training data cannot be linked back to individuals, thereby meeting key privacy obligations under laws such as GDPR. Erasing data after training may limit exposure but does not protect it during the training process. Ensuring data quality improves accuracy but does not mitigate privacy risk.
Hashing protects data integrity but does not guarantee anonymity, as hashes can sometimes be reversed or correlated. Therefore, anonymization is the recommended control for protecting personal data in AI training.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Methods) ISACA AI Security Management - Data Anonymization Practices


NEW QUESTION # 41
Which of the following is the GREATEST benefit of performing AI security risk assessments?

Answer: A

Explanation:
AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls. Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Assessment & Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.


NEW QUESTION # 42
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