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Data Loss Prevention

How to Cut DLP False Positives Without Weakening Protection

Jul 4, 2024 · 6 min read
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Siberson Team
Jul 4, 2024 · 6 min read

A DLP tool that cries wolf gets ignored — and eventually disabled. Reducing false positives is not about protecting less; it is about being precise. Five techniques do most of the work.

1. Enforce on labels, not raw patterns

When policies act on classification labels rather than guessing from content, the biggest source of noise disappears. A file marked Public simply is not treated like a Confidential one.

2. Add context to every rule

Combine the label with user, device, destination and scenario. Sending a customer list to a known processor is fine; sending it to a personal address is not. Context tells the two apart.

3. Use fingerprinting and OCR wisely

Document fingerprinting catches your proprietary content precisely, and endpoint OCR reads text inside images and scans so sensitive data cannot hide in a screenshot. Together they raise accuracy, not noise.

4. Warn before you block

A justification prompt turns many would-be incidents into teachable moments, and reserves hard blocks for the genuinely dangerous. It also builds user trust in the programme.

Data Loss PreventionVerikor
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