AI DLP

AI DLP extends data-loss prevention to the AI layer. It watches what goes into prompts, file uploads, and AI features, and intervenes before submission by redacting the sensitive part, warning the user, or blocking the request. The goal is to keep personal, client, and confidential data from reaching AI services that sit outside the organization's control.

Traditional DLP was built for email, file shares, endpoints, and network traffic. It struggles with AI use because the risky action is text typed or pasted into an encrypted browser session, often in a tool the security team never approved. By the time that text shows up as a network event, it has already been sent. Effective AI DLP acts at the point of entry, before the prompt is submitted.

What AI DLP typically looks for: personal information such as names, government ID numbers, and contact details; health information; financial and account data; credentials and API keys; source code; and confidential business material. Responses are usually graduated: redact and continue, warn and let the user decide, or block outright for the highest-risk data.

The design goal is preserving the workflow. Strip the client name and the key, let the request proceed, log the event. That turns AI usage from an unknown into an inventory, and policy violations into coaching moments with evidence. Blocking every AI tool tends to push employees onto personal accounts and personal devices, where no control applies.

When evaluating an AI DLP control, ask: Does it cover the AI tools employees actually use, including unapproved ones? Does it act before submission, or only log afterward? Can it redact instead of only blocking? How are false positives handled? And what do administrators see: which tools are in use, what was caught, and in which teams?