Improving Data Loss Prevention accuracy with AI-powered context analysis

This post introduces a self-improving AI-powered algorithm for Cloudflare's Data Loss Prevention (DLP) solution to reduce false positives. The algorithm leverages Workers AI for text embedding and Vectorize for nearest neighbor search and storage. It learns from customer feedback (false positive reports) to improve future detection accuracy. The system uses online clustering to reduce storage needs and Cloudflare Queues for asynchronous indexing. Privacy is maintained through data redaction before embedding and customer-specific private namespaces. Latency increases are associated with AI context analysis, and future improvements include multilingual support and enhanced transparency. AI context analysis is currently available for Gateway HTTP traffic and will be extended to CASB and Email Security.