Many data leaks are not attacks but mistakes: a customer list to the wrong address, a sharing link for everyone, a contract in a private cloud store, a section of source code in a public AI chat. Data loss prevention targets exactly that.
How DLP works
DLP rules detect sensitive content by patterns, such as card or social security numbers, by keywords or by sensitivity labels. Depending on the rule, the system informs the user, asks for a justification, blocks the action or logs it for later review.
Channels
Email and sharing in collaboration platforms are the most common channels. Add endpoints with USB storage and printing, uploads to unapproved cloud services and, increasingly, input into AI tools. With shadow AI in particular, DLP is often the only technical means to see which content flows into external services.
Rollout without disruption
Starting DLP with hard blocks paralyses operations and creates workarounds. A step-by-step approach works: rules in audit mode first, analysis of hits, adjustment, then user notifications, and blocks only for clear-cut cases. The basis is a classification defining what is to be protected at all.
Context
DLP does not replace a clean permission concept: what someone cannot see, they cannot pass on. And it complements security awareness, because a well-explained notice at the moment of the mistake achieves more than any training.