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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Management | 31% | - AI threat and vulnerability management
|
| AI Technologies and Controls | 38% | - Trust, safety and compliance controls
|
| AI Governance and Program Management | 31% | - Stakeholder considerations and governance frameworks
|
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NEW QUESTION # 82
Which of the following is the BEST reason to involve a diverse team when evaluating the data used within an AI system?
Answer: A
Explanation:
Involving a diverse team provides broader perspectives, experiences, and expertise during evaluation of AI data. This helps identify hidden biases, ethical concerns, security issues, and other risks that might otherwise be overlooked by a less diverse review group.
NEW QUESTION # 83
The PRIMARY benefit of implementing moderation controls in generative AI applications is that it can:
Answer: A
Explanation:
AAISM materials identify the primary benefit of moderation controls in generative AI systems as their ability to filter out harmful, offensive, or inappropriate content before it is delivered to users. This safeguards organizational reputation, compliance, and user trust. While moderation may indirectly support compliance with privacy requirements, its main function is ensuring that outputs align with ethical and safety standards.
Moderation does not enhance creativity or response speed. Its primary value is in controlling the quality of generated outputs by blocking harmful content.
References:
AAISM Study Guide - AI Technologies and Controls (Moderation and Output Controls) ISACA AI Security Management - Harmful Content Mitigation in Generative AI
NEW QUESTION # 84
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?
Answer: B
Explanation:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.
NEW QUESTION # 85
A global financial organization wants to deploy an AI solution for credit scoring. Which of the following should the organization do FIRST?
Answer: D
Explanation:
Ensuring the solution complies with applicable laws and regulations is the foundational step before any deployment activities. Regulatory compliance defines permissible data use, model behavior, and governance requirements, which must be established before conducting assessments, testing strategies, or documentation.
NEW QUESTION # 86
A financial institution plans to deploy an AI system to provide credit risk assessments for loan applications.
Which of the following should be given the HIGHEST priority in the system's design to ensure ethical decision-making and prevent bias?
Answer: C
Explanation:
In AI governance frameworks, credit scoring is treated as a high-risk application. For such systems, the highest-priority safeguard is human oversight to ensure fairness, accountability, and prevention of bias in automated decisions.
The AI Security Management™ (AAISM) domain of AI Governance and Program Management emphasizes that high-impact AI systems require explicit governance structures and human accountability. Human-in-the- loop design ensures that final decisions remain the responsibility of human experts rather than being fully automated. This is particularly critical in financial contexts, where biased outputs can affect individuals' access to credit and create compliance risks.
Official ISACA AI governance guidance specifies:
High-risk AI systems must comply with strict requirements, including human oversight, transparency, and fairness.
The purpose of human oversight is to reduce risks to fundamental rights by ensuring humans can intervene or override an automated decision.
Bias controls are strengthened by requiring human review processes that can analyze outputs and prevent unfair discrimination.
Why other options are not the highest priority:
A). Regular updates improve accuracy but do not guarantee fairness or ethical decision-making. Model drift can introduce new bias if not governed properly.
B). Appeals mechanisms are important for accountability, but they operate after harm has occurred.
Governance frameworks emphasize prevention through human oversight in the decision loop.
D). Restricting criteria to "objective metrics" is insufficient, as even objective data can contain hidden proxies for protected attributes. Bias mitigation requires monitoring, testing, and human oversight, not only feature restriction.
AAISM Domain Alignment:
Domain 1 - AI Governance and Program Management: Ensures accountability, ethical oversight, and governance structures.
Domain 2 - AI Risk Management: Identifies and mitigates risks such as bias, discrimination, and lack of transparency.
Domain 3 - AI Technologies and Controls: Provides the technical enablers for implementing oversight mechanisms and bias detection tools.
References from AAISM and ISACA materials:
AAISM Exam Content Outline - Domain 1: AI Governance and Program Management (roles, responsibilities, oversight).
ISACA AI Governance Guidance (human oversight as mandatory in high-risk AI applications).
Bias and Fairness Controls in AI (human review and intervention as a primary safeguard).
NEW QUESTION # 87
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