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NEW QUESTION # 191
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 # 192
Which of the following is the BEST approach for minimizing risk when integrating acceptable use policies for AI foundation models into business operations?
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 # 193
Which of the following AI data management techniques involves creating validation and test data?
Answer: D
Explanation:
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.
NEW QUESTION # 194
A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
Which of the following attacks is the HIGHEST priority to protect against?
Answer: D
Explanation:
AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces
NEW QUESTION # 195
A large pharmaceutical company using a new AI solution to develop treatment regimens is concerned about potential hallucinations with the introduction of real-world data. Which of the following is MOST likely to reduce this risk?
Answer: A
Explanation:
AAISM materials identify human-in-the-loop governance as the most effective safeguard against risks such as hallucinations in AI systems used in high-stakes domains like healthcare. By ensuring that human experts validate outputs before they influence patient treatment decisions, organizations preserve accountability, safety, and accuracy. Penetration testing is a cybersecurity measure, not relevant to hallucination risk. AI impact analysis helps evaluate systemic effects but does not directly prevent faulty outputs. Data validation improves input quality but cannot fully prevent generative hallucinations. The key safeguard is human-in-the- loop oversight.
References:
AAISM Study Guide - AI Governance and Program Management (Human Oversight in High-Risk AI) ISACA AI Security Management - Mitigating Hallucinations in Generative AI
NEW QUESTION # 196
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