The DLP Accuracy Problem: How AI Finally Fixes False Positives - 10 de junio de 2026 - TecnoWebinars.com

Most DLP programs have an accuracy problem — and most security teams have learned to live with it. Broad 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. In 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. We'll cover: • Why legacy DLP architectures are structurally prone to false positives • What AI-supervised classification actually does under the hood • How to evaluate DLP accuracy claims – and what questions to ask any vendor • A live look at what near-100% accuracy looks like in practice Whether 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.