Why a separate category
Traditional DLP is organised around files and channels.
Its model is that data lives in documents and leaves through enumerable exits: email attachment, USB, print, cloud sync, upload.
Text typed into a browser composer is none of those. It never becomes a file, never traverses a monitored channel, and never matches a rule — so a fully functioning endpoint DLP deployment reports nothing while thousands of records flow to a third party every week.
What the system does
Five steps, and the third is where products differ.
The distinguishing property is timing. Inspection happens before the message is sent, which is the only point at which prevention rather than after-the-fact alerting is possible.
- 01
Capture
The outbound text block and its destination, at the composer.
- 02
Detect
Sensitive data in it — patterns, contextual models, custom rules.
- 03
Classify
Against compliance frameworks, scored by whether the destination is sanctioned.
- 04
Enforce
Log, warn inline, or block the submit.
- 05
Report
A record to the console and the SIEM.
The two architectures
Where step two executes is the whole evaluation.
Which one a vendor uses is the single most consequential question in an AI DLP evaluation, and it is not always in the datasheet.
Cloud classification
The prompt is sent to a service for inspection. Heavier models, no endpoint footprint — and the content now exists in another vendor’s systems.
On-device classification
Detection runs locally and only a derived, redacted record is transmitted. Harder to build, and the content never leaves the machine.
Related terms