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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
Which of the following would BEST protect trade secrets related to AI technologies during their life cycle?

A. Patenting AI algorithms along with data sets
B. Enforcing trademark rights in AI systems
C. Restricting access to sensitive data
D. Introducing watermarks when generating AI output

Answer: C

Explanation:
Restricting access to sensitive data and artifacts (e.g., training data, feature stores, model weights, prompts, system designs) using least-privilege, segregation, encryption, and monitoring is the most effective way to protect trade secrets throughout the AI lifecycle. Patents require public disclosure, trademarks protect branding (not secrets), and output watermarks help provenance/abuse deterrence but do not secure underlying proprietary know-how.
References: AI Security Management (AAISM) Body of Knowledge: Information Protection for AI- Access Control, Segmentation, and Secrets Management; AAISM Study Guide: Lifecycle Security of AI Artifacts and Trade-Secret Safeguards.

NEW QUESTION # 113
Within an incident handling process, which of the following would BEST help restore end-user trust in an AI system?

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

Answer: D

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 # 114
A large financial services organization is integrating a third-party AI solution into its critical fraud detection system. Which of the following is the BEST way for the organization to reduce risk associated with AI vendor and supply chain dependencies?

A. Focusing on performance testing to ensure the solution meets operational requirements
B. Establishing contractual agreements requiring vendors to provide evidence of secure development practices
C. Implementing isolated virtual environments to validate the integration of the fraud detection system with the solution
D. Conducting annual vulnerability assessments of the fraud detection system after integration

Answer: B

Explanation:
AAISM emphasizes supplier assurance through contractual obligations as the foundational control for AI supply chain risk. Contracts should require verifiable evidence of secure development practices (e.g., secure SDLC, model and data provenance documentation, SBOM/MBOM where applicable, vulnerability disclosure, patch SLAs, audit rights, incident notification, and regulatory compliance assertions). This creates enforceable, continuous assurance beyond point-in-time tests.

NEW QUESTION # 115
Which of the following is the BEST reason to immediately disable an AI system?

A. Slow model performance
B. Excessive model drift
C. Overly detailed model outputs
D. Insufficient model training

Answer: B

Explanation:
According to AAISM lifecycle management guidance, the best justification for disabling an AI system immediately is the detection of excessive model drift. Drift results in outputs that are no longer reliable, accurate, or aligned with intended purpose, creating significant risks. Performance slowness and overly detailed outputs are operational inefficiencies but not critical shutdown triggers. Insufficient training should be addressed before deployment rather than after. The trigger for immediate deactivation in production is excessive drift compromising reliability.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Model Drift Management) AI Security Management Study Guide - Disabling AI Systems

NEW QUESTION # 116
Which of the following would BEST ensure safe implementation of AI systems?

A. Phased implementation with human-in-the-loop validation before full rollout.
B. Compliance with the AI risk management framework and applicable regulations.
C. Establishment of an AI program steering committee to govern AI implementation.
D. Selection of AI projects that align with business objectives.

Answer: A

Explanation:
A phased implementation combined with human-in-the-loop validation allows the organization to monitor AI behavior, identify issues early, validate outputs, and maintain human oversight before full deployment. This approach most effectively ensures safe and controlled implementation of AI systems.

NEW QUESTION # 117
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