DLP for AI is the set of controls that stop sensitive data from reaching a model — in a prompt, a pasted excerpt, an uploaded file, or a tool response an agent forwards — and from coming back out in a completion. It maps an existing enterprise budget line onto a new egress path, which is usually how it gets funded.
Conventional DLP was built for channels with structure: an email has a recipient, a file transfer has a destination, an upload has a filename. A prompt has none of that. It is free text, generated in the moment, and the sensitive content may be a paraphrase that matches no pattern. The employee summarizing a contract in their own words has disclosed its terms without triggering a single regular expression.
How it differs from traditional DLP. Pattern matching finds structured identifiers — card numbers, national IDs, account formats. Prompt content is unstructured and often reformulated, so detection has to reason about meaning as well as shape. The channel is also bidirectional in a way file transfer is not: the completion can carry sensitive content outward just as the prompt carries it in.
How it differs from shadow AI controls. Shadow AI is about which tools people use. DLP is about what goes into the tools they are allowed to use. Blocking unsanctioned services does nothing about the sanctioned one, and the sanctioned one carries the majority of the volume.
AI-specific DLP requires semantic understanding of prompts, not just pattern matching.
Enforcing it at the egress point
The practical reason DLP for AI belongs at the gateway rather than the endpoint is coverage. An endpoint agent governs the machines it is installed on. The network path is what every call shares — the employee in a browser, the application calling an API, the agent forwarding a tool response it never read. Put the control where they converge and the estate is covered once.
What that requires in practice is inline inspection in both directions, classification that reasons about meaning rather than pattern alone, and redaction as an option alongside refusal, because a control that only blocks gets routed around. Egress control in the Trust Fabric covers the mechanism, the enterprise AI firewall guide the broader pattern, and APERION Shield the local enforcement point. In healthcare the same control is what keeps PHI inside the perimeter. Which rules apply, and the record that they did, is a question of policy as code.
Related terms
Prompt injection · Information barrier · AI sovereignty · HIPAA Security Rule and AI · AI guardrails · Full glossary
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