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ISACA AAISM Exam Overview:

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

Topic	Details
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 (Q102-Q107):

NEW QUESTION # 102
An AI fraud detection system frequently flags legitimate transactions as suspicious, leading to customer dissatisfaction and operational obstacles. Which of the following is the BEST way to mitigate this issue without compromising security?

A. Conduct a model review to improve accuracy and introduce explainability techniques.
B. Increase the AI model's sensitivity threshold to reduce false positives.
C. Disable the AI model temporarily and revert to manual fraud checks.
D. Collect more data points such as social media activity to improve fraud predictions.

Answer: A

Explanation:
Conducting a model review helps identify why legitimate transactions are being incorrectly flagged and enables improvements to model accuracy. Adding explainability techniques also helps analysts and stakeholders better understand decision outcomes, reducing operational issues while maintaining effective fraud detection capabilities.

NEW QUESTION # 103
Within an incident handling process, which of the following would BEST help restore end-user trust in an AI system?

A. Remediation of the AI system based on lessons learned
B. The AI model's outputs are validated by team members
C. The AI model prioritizes incidents based on business impact
D. AI is used to monitor incident detection and alerts

Answer: A

Explanation:
AAISM highlights that post-incident remediation and demonstrating lessons learned is essential to restoring trust. Governance guidance specifies that stakeholders regain confidence only when organizations show clear corrective actions, transparency, and improvements to prevent recurrence.
Validating outputs (B) supports accuracy but is not trust-restoring. Monitoring (C) and prioritization (D) relate to operations, not trust rebuilding.
References: AAISM Study Guide - AI Governance; Incident Response and Trust Restoration.

NEW QUESTION # 104
The GREATEST security risk associated with the use of pre-trained models from public repositories is that the model may:

A. not be compatible with commercial use.
B. have lower accuracy than a custom-built model.
C. require significant maintenance resources.
D. contain hidden triggers activated by specific inputs.

Answer: D

Explanation:
Pre-trained models from public repositories may contain hidden backdoors or malicious triggers intentionally inserted during training. These triggers can activate under specific conditions and cause unauthorized or harmful behavior, making this the greatest security risk.

NEW QUESTION # 105
Which AI data management technique involves creating validation and test data?

A. Learning
B. Annotating
C. Splitting
D. Training

Answer: C

Explanation:
AAISM describes data splitting as the process of dividing datasets into:
- training
- validation
- test sets
This is essential for reducing overfitting and ensuring robust evaluation.

NEW QUESTION # 106
For a life insurance company deploying AI for fraud detection, which factor is MOST critical?

A. Adaptability
B. Explainability
C. Robustness
D. Accuracy

Answer: C

Explanation:
AAISM emphasizes robustness as the key requirement for fraud-detection systems because they must resist adversarial manipulation, data poisoning, spoofing, and input tampering.
Accuracy (B) matters but does not protect against adversarial attacks. Explainability (C) is important but secondary. Adaptability (D) is useful but not the top security requirement.
References: AAISM Study Guide - AI Robustness; Fraud Detection and Adversarial Resistance.

NEW QUESTION # 107
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