Most organizations think they’re “doing” data discovery and classification. The hard truth: most aren’t doing it well enough to matter. Regex rules, surveys, and half-baked sampling won’t cut it. False positives drown teams. Critical data types go unseen. Sensitive data hides in unstructured files, code repos, chat logs, and AI training sets — outside the reach of legacy tools.
Join BigID for a live, unfiltered look at why not all discovery is created equal. We’ll break down the pitfalls of generic approaches and show what next-generation discovery really looks like:
- Accuracy that matters: Go beyond pattern-matching to identity-aware and AI-powered classification that slashes false positives.
- Coverage without compromise: Discover all your data — structured, unstructured, SaaS, cloud, on-prem, even AI training sets — not just a convenient sample.
- Context with outcomes: Classify data with meaning: not just “what it is” but “whose it is,” “where it lives,” and “what to do with it.”
- Continuous, not one-and-done: Track data across its lifecycle, flag sensitivity shifts, and automate remediation in real time.
If you’re tired of generic “check-the-box” discovery, this session will show you how to raise the bar — and why the difference between “good enough” and “accurate, contextual, and scalable” discovery can make or break your security, privacy, and AI strategy.