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Which of the following AI-driven systems should have the MOST stringent recovery time objective (RTO)?
See the explanation below.
AAISM risk guidance notes that the most stringent recovery objectives apply to industrial control systems, as downtime can directly disrupt critical infrastructure, manufacturing, or safety operations. Health support systems also require high availability, but industrial control often underpins safety-critical and real-time environments where delays can result in catastrophic outcomes. Credit risk models and navigation systems are important but less critical in terms of immediate physical and operational impact. Thus, industrial control systems require the tightest RTO.
AAISM Study Guide -- AI Risk Management (Business Continuity in AI)
ISACA AI Security Management -- RTO Priorities for AI Systems
An organization utilizes AI-enabled mapping software to plan routes for delivery drivers. A driver following the AI route drives the wrong way down a one-way street, despite numerous signs. Which of the following biases does this scenario demonstrate?
See the explanation below.
AAISM defines automation bias as the tendency of individuals to over-rely on AI-generated outputs even when contradictory real-world evidence is available. In this scenario, the driver ignores traffic signs and follows the AI's instructions, showing blind reliance on automation. Selection bias relates to data sampling, reporting bias refers to misrepresentation of results, and confirmation bias involves interpreting information to fit pre-existing beliefs. The most accurate description is automation bias.
AAISM Exam Content Outline -- AI Risk Management (Bias Types in AI)
AI Security Management Study Guide -- Automation Bias in AI Use
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:
See the explanation below.
AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.
AAISM Study Guide -- AI Risk Management (Bias and Ethics Validation)
ISACA AI Security Management -- Ethical AI Practices
Which of the following is the BEST reason to immediately disable an AI system?
See the explanation below.
According to AAISM lifecycle management guidance, the best justification for disabling an AI system immediately is the detection of excessive model drift. Drift results in outputs that are no longer reliable, accurate, or aligned with intended purpose, creating significant risks. Performance slowness and overly detailed outputs are operational inefficiencies but not critical shutdown triggers. Insufficient training should be addressed before deployment rather than after. The trigger for immediate deactivation in production is excessive drift compromising reliability.
AAISM Exam Content Outline -- AI Governance and Program Management (Model Drift Management)
AI Security Management Study Guide -- Disabling AI Systems
Which of the following is a key risk indicator (KRI) for an AI system used for threat detection?
See the explanation below.
AAISM materials emphasize that in operational AI systems, key risk indicators (KRIs) must reflect risks to performance and reliability rather than technical design factors alone. In the case of threat detection, the most relevant KRI is the frequency of system overrides by human analysts, as this indicates a lack of trust, frequent false positives, or poor detection accuracy. Training epochs, model depth, and training time are technical metrics but do not directly measure operational risk. Analyst overrides represent a practical measure of system effectiveness and risk.
AAISM Study Guide -- AI Risk Management (Operational KRIs for AI Systems)
ISACA AI Security Management -- Monitoring AI Effectiveness
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