Data Loss Prevention

Data Loss Prevention, abbreviated DLP, is the family of policies and technologies that detect and block unauthorized exfiltration of sensitive data — by users, applications, or AI systems — across endpoints, network boundaries, cloud platforms, and now LLM prompts and responses. Traditional DLP vendors include Symantec (now Broadcom), Forcepoint, McAfee Skyhigh, Microsoft Purview (formerly Microsoft Information Protection), Proofpoint, and Digital Guardian. The AI era has produced a new generation focused specifically on LLM prompts and responses: Lakera Guard, Robust Intelligence AI Firewall, Prompt Security, Lasso Security, and HiddenLayer all monitor the prompt-response stream for PII, IP, trade secrets, source code, and policy violations. A practical AI-era DLP setup: place an inline proxy between users and the LLM, run every prompt and response through a battery of classifiers (PII, IP, credentials, prompt-injection patterns, off-topic), log every detection, block or warn based on policy, and emit telemetry to the SIEM. The hardest part is not the detection — it is the policy design: which data categories are absolutely-no-LLM versus okay-with-redaction versus fully-allowed? That mapping is the actual DLP work; the technology is the enforcement. AI governance teams pair DLP with guardrails on the prompt/response layer and pre-index PII redaction on the ingestion layer — defense in depth across the full path.

DLP is what Oxcyon's sensitivity engine has always been: Centralpoint has classified, restricted, and audited sensitive data flow for 25 years — DLP for LLM prompts is the AI-era extension of the same engine that has protected enterprise content for FedEx, Samsung, and the US Congress. DLP runs on-premise, tokens meter per skill, and DLP-protected chatbots deploy through one line of JavaScript.


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