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DESCRIPTION:Most DLP programs have an accuracy problem — and most security teams have learned to live with it.\nBroad pattern matching. Thousands of alerts. Analysts spending hours triaging noise instead of investigating real incidents. The false positive rate isn't a bug in your configuration – it's a fundamental limitation of how legacy DLP was built.\nIn this webinar, we break down why traditional DLP generates so much noise, what "good" accuracy actually looks like, and how AI-powered supervision is changing the bar – automatically enriching and prioritizing violations with full data context, confidence scoring, and remediation guidance, without requiring manual tuning at every step.\nWe'll cover:\n• Why legacy DLP architectures are structurally prone to false positives\n• What AI-supervised classification actually does under the hood\n• How to evaluate DLP accuracy claims – and what questions to ask any vendor\n• A live look at what near-100% accuracy looks like in practice\nWhether you're re-evaluating your DLP stack or trying to get more signal out of what you already have, this session gives you a concrete framework and a clear picture of where the technology is headed.\n
DTSTART:20260610T150000
DTEND:20260610T160000
DTSTAMP:20260425T055906Z
LOCATION:Online
PRIORITY:5
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SUMMARY;LANGUAGE=es-es:Tecnowebinars.com - :: The DLP Accuracy Problem: How AI Finally Fixes False Positives
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UID:e4ba2b031f8a17c88255cbd3416f86f6 Tecnowebinars.com
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