False Positive
When an AI model incorrectly predicts a positive class for an instance that is actually negative (Type I error).
A scenario where the model flags benign cases as malicious - common in security, fraud, or medical screening. High false-positive rates can overwhelm human reviewers, erode trust, and incur unnecessary costs. Governance requires monitoring precision, setting acceptable thresholds, and implementing human-review triggers to batch or prioritize alerts effectively.
A credit-card fraud-detection AI has a 10% false-positive rate, meaning 1 in 10 legitimate transactions is blocked. The bank sets up a rapid-response team to review false positives within 5 minutes, adjusts model thresholds to reduce false alarms during peak shopping hours, and communicates pre-authorization rules to customers to minimize inconvenience.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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