What Is Knowledge Egress?
Knowledge egress is the flow of proprietary information out of an organization through AI usage: prompts, documents, traces, and corrections sent to external model providers. Unlike a one-time breach, it is continuous, authorized-looking, and cumulative. Without a control point at the network boundary it is invisible to security teams, because each individual request looks legitimate.
Who needs this
CISOs and data-protection officers first, but increasingly anyone who owns proprietary know-how: the prompts your teams refine, the corrections they make, and the internal documents they paste are training-grade material about how your business works. Legal teams meet the same issue as a GDPR question when personal data crosses into unauthorized processors.
Why classic DLP misses it
Traditional data-loss prevention watches for files leaving through email, uploads, and USB drives. Knowledge egress travels as text inside TLS-encrypted API calls to domains your firewall categorizes as productivity tools. A Microsoft survey found 78% of AI users bring their own tools to work; every one of those sessions is an egress channel no file-transfer monitor sees.
The workable control point is the AI boundary itself: a gateway that sees every prompt before it leaves, can mask or block classified patterns inline, and records what actually flowed. Blocking AI wholesale just pushes usage to personal devices; seeing and governing the flow is the sustainable posture.
Data point · a DLP finding at the boundary, Kyde Gateway
PII detected · email address in outbound prompt
action · masked before forwarding
context · redacted by role, entry hash-chained into the ledger
modes · observe (alert) or prevent (block inline)
Two engines inspect outbound traffic: regex patterns plus a neural detector, applied per policy at the boundary. What you cannot see, you cannot govern.
See it running, not just defined.
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