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The ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) certification is the way to go in the modern ISACA era. Success in the ISACA AAISM exam of this certification plays an essential role in an individual's future growth. Nowadays, almost every tech aspirant is taking the test to get ISACA AAISM Certification and find well-paying jobs or promotions. But the main issue that most of the candidates face is not finding updated ISACA AAISM practice questions to prepare successfully for the ISACA AAISM certification exam in a short time.

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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q398-Q403):

NEW QUESTION # 398
An organization has requested a developer to apply AI algorithms to existing modules in order to improve customer service quality. At this stage, which of the following should be considered FIRST?

A. IT management may need to revise the service agreement if AI behavior cannot be predefined
B. The organization may need to explain the performance of the applied AI algorithm
C. The developer may need to be held accountable for business inquiries raised by customers
D. Project sponsors may need to agree on a phased approach in order to ensure safe release

Answer: A

Explanation:
According to AAISM governance principles, when AI functionality is added to existing services, the first consideration is contractual and service-level accountability. If AI outputs cannot be predefined, the existing service agreements may no longer reflect performance responsibilities or liability. Revising or updating the agreement ensures governance alignment, accountability, and risk management for AI-driven behavior. Phased approaches and performance explanations are valuable but occur later in project management. Developer accountability for customer inquiries is not a primary governance step. The most immediate consideration is revising service agreements when AI introduces new uncertainties.

NEW QUESTION # 399
A health services organization is developing a proprietary generative AI chatbot to assist patients with medical devices. Which of the following should be the organization's HIGHEST priority?

A. Maximizing the amount of training data
B. Tuning algorithms used in the AI model
C. Selecting the appropriate training data
D. Maximizing neural network size

Answer: C

Explanation:
AAISM prioritizes training data suitability--lawful sourcing, provenance, quality, representativeness, and safety--especially in health-related applications. The correctness and appropriateness of training data determine clinical safety, reduction of harmful outputs, and compliance with data protection/sector obligations. Larger models or more data do not compensate for inappropriate or low-quality datasets; tuning is secondary to ensuring the right data with rigorous curation, labeling quality, and guardrails aligned to patient safety requirements.

NEW QUESTION # 400
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

A. Absence of metrics and dashboard for analysts
B. Insufficient model validation and change control processes
C. Excessive reliance on external consultants for model design
D. Lack of sufficient computing resources for the AI system

Answer: B

Explanation:
AAISM requires formal model change governance: documented justification, risk assessment, validation
/verification (V&V), approvals, and post-deployment monitoring when altering features, thresholds, or signals. Disabling risk indicators to reduce false positives without rigorous validation and controlled rollout reflects a failure in model validation and change control, which AAISM treats as a core safeguard against unintended harms and regulatory breaches.
References: AI Security Management™ (AAISM) Body of Knowledge - Model Risk Governance; Change Management & Approvals; Validation/Verification Requirements. AAISM Study Guide - Control Gates for Feature/Threshold Changes; Post-Change Monitoring and Backout Criteria.

NEW QUESTION # 401
A critical AI system shows biased outcomes. What is the BEST course of action?

A. Retrain the model with a new diverse dataset
B. Conduct audits of data and model
C. Activate the kill switch
D. Perform root cause analysis to identify mitigation

Answer: D

Explanation:
AAISM emphasizes root cause analysis as the primary step when AI behavior deviates unexpectedly.
RCA determines whether issues stem from:
- data drift
- model drift
- poisoned data
- missing features
- logic errors
- configuration changes

NEW QUESTION # 402
Which of the following is the BEST mitigation control for membership inference attacks on AI systems?

A. Cybersecurity-oriented red teaming
B. Model ensemble techniques
C. Differential privacy
D. AI threat modeling

Answer: C

Explanation:
Membership inference attacks attempt to determine whether a particular data point was part of a model's training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.

NEW QUESTION # 403
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