Monitoring What Employees Share With AI Tools
Security Boulevard, Monday, October 19th, 2026
Credentials, code, customer records and financials dominate AI-tool leakage; each needs a different detection approach. Kovrr post.
Kovrr argues four data categories account for most of what leaves organizations through AI assistants.
Credentials are easy to detect by their high-entropy prefixes and can be blocked outright, while source code is common to paste, so it should trigger a warning rather than a block unless it contains internal hostnames or package names.
Customer records should be flagged by record count, not by any personal data, and financial material can only be caught through provenance, such as a paste shortly after a finance export.
Traditional DLP misses browser pastes, so this vendor-authored post proposes in-browser classification that sends only metadata off the device.