From the course: CompTIA SecAI+ (CY0-001) Cert Prep

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AI risks

AI risks

From the course: CompTIA SecAI+ (CY0-001) Cert Prep

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AI risks

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Every AI initiative carries uncertainty. Models often learn from data that you did not curate line by line, grow more confident than their evidence warrants, and act at machine speed in contexts that might surprise their builders. Minor oversights can ripple outward. A mislabeled training record begins to skew predictions. A prompt leaks a fragment of private text or a slow-moving drift in user behavior erodes accuracy until a headline calls it out. Risk management, therefore, is not a checkpoint at the end of a project, but a continuous thread that runs from the first sketch of a use case to the day the system retires. The shape and scale of AI risk depends upon impact, exposure, and uncertainty. Impact asks, how much harm could a faulty decision cause? Is it an errant search suggestion or a denied mortgage? Exposure looks at the system's reach, counting users, transactions, and integrations with other workflows. Uncertainty measures what you do not yet know about data quality, model…

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