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Valid AAISM Test Materials | New AAISM Test Forum

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which of the following BEST enables an organization to maintain visibility to its AI usage?

A. Measuring the impact of AI implementation using key performance indicators (KPIs)
B. Maintaining a comprehensive inventory of AI systems and business units that leverage them
C. Maintaining a monthly dashboard that captures all AI vendors
D. Ensuring the board approves the policies and standards that define corporate AI strategy

Answer: B

Explanation:
The AAISM framework stresses that the most effective way to maintain oversight of organizational AI usage is by maintaining a comprehensive inventory of all AI systems and the business units using them. Such an inventory provides a centralized, transparent record of where AI is deployed, ensuring accountability, monitoring, and compliance. While board approval, dashboards, and KPIs are important governance tools, they do not provide holistic visibility across the enterprise. The inventory ensures traceability and governance alignment, making it the best method to maintain visibility of AI usage.
References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories) ISACA AI Security Management - Centralized Oversight of AI Assets

NEW QUESTION # 22
Which of the following key risk indicators (KRIs) is MOST relevant when evaluating the effectiveness of an organization's AI risk management program?

A. Percentage of critical business systems with AI components
B. Number of AI models deployed into production
C. Percentage of AI projects in compliance
D. Number of AI-related training requests submitted

Answer: C

Explanation:
AAISM identifies percentage of AI projects in compliance as the most relevant KRI for evaluating AI risk management effectiveness. This metric directly reflects adherence to governance, regulatory, and security requirements. The number of models deployed (A) or systems with AI components (B) indicate scale, not risk management quality. Training requests (D) show awareness levels but do not measure effectiveness of risk management. Compliance percentage provides a direct, measurable indication of how well risks are being governed and mitigated.
References:
AAISM Exam Content Outline - AI Risk Management (Risk Metrics and Compliance) AI Security Management Study Guide - Key Risk Indicators in AI Programs

NEW QUESTION # 23
In a new supply chain management system, AI models used by participating parties are interactively connected to generate advice in support of management decision making. Which of the following is the GREATEST challenge related to this architecture?

A. Explaining the overall benefit of the system to stakeholders
B. Establishing clear lines of responsibility for AI model outputs
C. Determining the aggregate risk of the system
D. Identifying hallucinations returned by AI models

Answer: B

Explanation:
The AAISM governance framework notes that in multi-party AI ecosystems, the greatest challenge is ensuring clear accountability for AI outputs. When models from different parties interact, responsibility for errors, bias, or harmful recommendations can be unclear, leading to disputes and compliance gaps. While aggregate risk assessment and error identification are significant, they are secondary to the fundamental governance requirement of establishing transparent lines of responsibility. Without defined accountability, no stakeholder can reliably manage or mitigate risks. Therefore, the greatest challenge in such a distributed architecture is responsibility for AI outputs.
References:
AAISM Study Guide - AI Governance and Program Management (Accountability in Multi-Party Systems) ISACA AI Governance Guidance - Roles and Responsibilities in AI Collaboration

NEW QUESTION # 24
Which of the following BEST describes the role of risk documentation in an AI governance program?

A. Demonstrating governance, risk, and compliance (GRC) for external stakeholders
B. Offering detailed analyses of technical risk and vulnerabilities
C. Outlining the acceptable levels of risk for AI-related initiatives
D. Providing a record of past AI-related incidents for audits

Answer: C

Explanation:
In AAISM governance guidance, risk documentation is described as the structured record that defines the organization's risk appetite and tolerance levels for AI initiatives. By outlining acceptable levels of risk, documentation ensures decision-makers can approve, monitor, and adjust AI projects within defined boundaries. While it may also serve audit functions, technical analysis, or communication to stakeholders, its primary role is to formalize risk acceptance thresholds and integrate them into governance and decision- making. This aligns directly with the governance requirement to align AI adoption with organizational risk appetite.
References:
AAISM Study Guide - AI Governance and Program Management (Risk Documentation and Appetite) ISACA AI Security Management - Governance, Risk and Compliance Integration

NEW QUESTION # 25
Which of the following controls BEST mitigates the risk of data poisoning?

A. Intrusion detection
B. Digital watermarking
C. Data validation
D. Data set restoration

Answer: C

Explanation:
The AAISM technical controls framework emphasizes data validation as the primary safeguard against data poisoning attacks. Poisoning occurs when attackers insert malicious or corrupted data into training sets.
Validation techniques verify the quality, authenticity, and consistency of input data before training, preventing compromised samples from corrupting the model. Restoration helps after compromise, watermarking protects ownership, and intrusion detection monitors networks rather than data quality. The most effective preventive measure is data validation.
References:
AAISM Study Guide - AI Technologies and Controls (Data Poisoning Mitigation) ISACA AI Security Management - Data Validation and Quality Controls

NEW QUESTION # 26
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