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Latest ISACA Advanced in AI Security Management (AAISM) Exam practice test & AAISM troytec pdf

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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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
From a risk perspective, which of the following is the MOST important step when implementing an adoption strategy for AI systems?

A. Conducting an AI risk assessment and updating the enterprise risk register
B. Implementing a robust risk analysis methodology tailored to AI-specific tasks
C. Establishing a comprehensive AI risk assessment framework
D. Benchmarking against peer organizations' AI risk strategies

Answer: A

Explanation:
AAISM guidance states that when adopting AI, the most important step is to conduct a risk assessment and update the enterprise risk register. This ensures AI-specific risks are identified, documented, and integrated into the organization's existing governance structures. Benchmarking peers provides context but does not address internal risk. Implementing methodologies and frameworks are important, but they precede or follow the assessment process. The decisive step that connects adoption to enterprise risk governance is updating the risk register with AI-specific risks.
References:
AAISM Study Guide - AI Risk Management (Integration with Enterprise Risk Management) ISACA AI Security Management - Risk Assessment and Register Updates

NEW QUESTION # 21
An organization develops and implements an AI-based plug-in for users that summarizes their individual emails. Which of the following is the GREATEST risk associated with this application?

A. Insufficient rate limiting for APIs
B. Data format incompatibility
C. Lack of application vulnerability scanning
D. Inadequate controls over parameters

Answer: D

Explanation:
According to AAISM risk management guidance, the greatest risk in AI applications handling personal communication data is inadequate parameter controls, which may allow unintended access, manipulation, or leakage of sensitive information. Plug-ins that interact with emails must enforce strict parameter validation and security restrictions to prevent unauthorized or manipulated inputs. While vulnerability scanning, format incompatibility, and API rate limiting are valid concerns, they are secondary. The primary risk is a lack of strong parameter controls that could expose sensitive content.
References:
AAISM Exam Content Outline - AI Risk Management (Application Security Risks) AI Security Management Study Guide - Plug-in and API Security Risks

NEW QUESTION # 22
An organization plans to apply an AI system to its business, but developers find it difficult to predict system results due to lack of visibility to the inner workings of the AI model. Which of the following is the GREATEST challenge associated with this situation?

A. Gaining the trust of end users through explainability and transparency
B. Continuing operations to meet expected AI security requirements
C. Determining average turnaround time for AI transaction completion
D. Assigning a risk owner who is responsible for system uptime and performance

Answer: A

Explanation:
AAISM materials identify explainability and transparency as the greatest challenge when models operate as
"black boxes" where inner logic is opaque. Inability to interpret how results are produced undermines the trust of business users, customers, regulators, and auditors. Explainability is emphasized as a critical governance requirement, because without it, ethical validation, accountability, and regulatory compliance are at risk.
Assigning risk owners or measuring transaction times are operational concerns, but they do not address the core trust deficit caused by lack of visibility. The greatest challenge in this situation is therefore the loss of end-user trust due to insufficient explainability.
References:
AAISM Study Guide - AI Governance and Program Management (Transparency and Explainability) ISACA AI Security Management - Ethical and Trust Considerations

NEW QUESTION # 23
An organization plans to implement a new AI system. Which of the following is the MOST important factor in determining the level of risk monitoring activities required?

A. The organization's number of AI system users
B. The organization's risk tolerance
C. The organization's risk appetite
D. The organization's compensating controls

Answer: B

Explanation:
AAISM risk management guidance clarifies that the organization's risk tolerance is the most important factor in determining how much monitoring is needed. Risk tolerance specifies the amount of risk the organization is willing to accept and defines the threshold for triggering monitoring or mitigation activities. Risk appetite is broader and strategic, while tolerance sets the operational limits. The number of users may influence scale, and compensating controls may affect resilience, but neither dictates monitoring intensity as directly as risk tolerance.
References:
AAISM Study Guide - AI Risk Management (Risk Appetite vs. Tolerance)
ISACA AI Security Management - Monitoring Based on Risk Tolerance

NEW QUESTION # 24
Which of the following is the BEST approach for minimizing risk when integrating acceptable use policies for AI foundation models into business operations?

A. Implement responsible development training and awareness
B. Establish AI model life cycle policy and procedures
C. Rely on the developer's enforcement mechanisms
D. Limit model usage to predefined scenarios specified by the developer

Answer: B

Explanation:
The AAISM guidance defines risk minimization for AI deployment as requiring a formalized AI model life cycle policy and associated procedures. This ensures oversight from design to deployment, covering data handling, bias testing, monitoring, retraining, decommissioning, and acceptable use. Limiting usage to developer-defined scenarios or relying on vendor mechanisms transfers responsibility away from the organization and fails to meet governance expectations. Training and awareness support cultural alignment but cannot substitute for structured lifecycle controls. Therefore, the establishment of a documented lifecycle policy and procedures is the most comprehensive way to minimize operational, compliance, and ethical risks in integrating foundation models.
References:
AAISM Study Guide - AI Governance and Program Management (Model Lifecycle Governance) ISACA AI Security Guidance - Policies and Lifecycle Management

NEW QUESTION # 25
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