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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 (Q46-Q51):

NEW QUESTION # 46
What BEST protects trade secrets related to AI technologies during their life cycle?

A. Watermarking AI output
B. Restricting access to sensitive data
C. Enforcing trademark rights
D. Patenting AI algorithms and data

Answer: B

Explanation:
AAISM emphasizes access control and data security as the strongest mechanisms to protect trade secrets, including:
- proprietary algorithms
- training datasets
- model weights
- internal design documentation

NEW QUESTION # 47
A vendor switched its chatbot's AI model without due diligence, causing unethical investment advice. What control BEST prevents this scenario?

A. Data minimization
B. Shared responsibility model
C. Master services agreement
D. Change management

Answer: D

Explanation:
AAISM stresses AI-specific change management as essential for vendor-driven or system-driven updates.
Proper change control includes:
* impact assessments
* ethical review
* risk evaluation
* approval checkpoints
* rollback plans
An MSA (A) supports contracts but does not manage operational change. Shared responsibility (C) describes roles, not change control. Data minimization (D) reduces exposure but doesn't control model updates.
References: AAISM Study Guide - AI Change Management; Vendor Model Update Controls.

NEW QUESTION # 48
Which of the following is the MOST important consideration for an organization that has decided to adopt AI to leverage its competitive advantage?

A. Develop a comprehensive risk management process to address AI-related issues
B. Develop a business case for the procurement of AI monitoring tools
C. Develop a comprehensive strategic roadmap for AI integration
D. Develop internal training programs on AI governance, risk, and compliance (GRC)

Answer: C

Explanation:
AAISM's governance guidance emphasizes that adopting AI for competitive advantage must begin with a comprehensive strategic roadmap for integration. This roadmap aligns AI adoption with business objectives, sets priorities, defines milestones, and ensures coordination across functions. Risk management, training, and tool procurement are essential, but they are tactical steps that follow once the strategic direction is defined. Without a roadmap, adoption becomes fragmented and risks misalignment with business strategy. The most important consideration at the adoption stage is therefore creating a strategic integration roadmap.

NEW QUESTION # 49
An organization is deploying an automated AI cybersecurity system. Which strategy MOST effectively minimizes human error and improves security?

A. Manual monitoring of alerts
B. Using historical data to train detection software
C. Conducting periodic penetration testing
D. Utilizing machine learning algorithms to ensure responsible use

Answer: B

Explanation:
AAISM states that the effectiveness of automated AI cybersecurity systems depends heavily on well-trained detection models using high-quality historical attack data.
Historical data improves:
- detection accuracy
- reduction of false positives
- reduction of human misinterpretation

NEW QUESTION # 50
An automotive manufacturer uses AI-enabled sensors on machinery to monitor variables such as vibration, temperature, and pressure. Which of the following BEST demonstrates how this approach contributes to operational resilience?

A. Scheduling repairs for critical equipment based on real-time condition monitoring
B. Automating equipment repairs without any human intervention
C. Performing regular maintenance based on manufacturer recommendations
D. Conducting monthly manual reviews of maintenance schedules

Answer: A

Explanation:
AAISM highlights that AI-enabled predictive maintenance improves operational resilience by using real-time sensor monitoring to schedule repairs based on actual conditions rather than fixed schedules. This prevents unexpected breakdowns, reduces downtime, and ensures continuity of operations. Regular maintenance based on recommendations is static and may not reflect real conditions. Manual reviews are slow and inefficient.
Full automation of repairs without human oversight is not realistic or safe in critical manufacturing. The approach that best demonstrates resilience is real-time condition-based repair scheduling.
References:
AAISM Study Guide - AI Risk Management (Operational Resilience and Predictive Maintenance) ISACA AI Security Management - AI for Critical Infrastructure Reliability

NEW QUESTION # 51
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