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NEW QUESTION # 132
When implementing a generative AI system, which of the following approaches will BEST prevent misalignment between the corporate risk appetite and tolerance?
Answer: D
Explanation:
AAISM governance guidance specifies that alignment between AI system adoption and organizational risk appetite is achieved by defining and monitoring acceptable levels of system risk. This ensures that generative AI operations remain within boundaries approved by leadership and compliance frameworks. While KPIs track performance, they do not ensure alignment with risk tolerance. AI impact assessments help identify risks but do not maintain continuous oversight. A risk register records risks but does not dynamically enforce acceptable thresholds. The most effective governance approach is to establish and monitor acceptable AI system risk levels.
References:
AAISM Study Guide - AI Governance and Program Management (Risk Appetite and Tolerance Alignment) ISACA AI Security Management - Generative AI Risk Governance
NEW QUESTION # 133
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?
Answer: C
Explanation:
AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may introduce new errors without addressing fairness. Securing training data protects confidentiality and integrity but does not resolve representational imbalance. Therefore, the best practice for reducing bias in ML is diversification of training datasets.
References:
AAISM Study Guide - AI Risk Management (Bias and Fairness in AI)
ISACA AI Security Management - Data Diversity and Representation Controls
NEW QUESTION # 134
An information security manager is tasked with preparing management reports regarding the risk mitigation controls applied to the organization's customer service chatbot. Which of the following is MOST critical to include in these reports?
Answer: D
Explanation:
Management reports should provide actionable recommendations that help decision-makers improve the effectiveness of risk mitigation controls. This information is critical for ongoing governance, risk reduction, and informed management oversight of the chatbot environment.
NEW QUESTION # 135
A SaaS-based LLM system has risks including prompt injection, data poisoning, and model exfiltration. What is the BEST way to ensure consistent risk treatment?
Answer: A
Explanation:
AAISM highlights the importance of an AI threat-control matrix that:
- maps threats (e.g., poisoning, exfiltration, injection)
- identifies required controls
- defines assurance/testing activities
- ensures consistent coverage across all AI components
NEW QUESTION # 136
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?
Answer: D
Explanation:
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.
References: AI Security Management™ (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.
NEW QUESTION # 137
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