Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Sensitive data detection was much easier when information lived inside structured databases. Tables, columns, field names, and predictable data types gave security teams a clear map of where sensitive information lived. But when sensitive data moved into documents and PDFs, that map disappeared. Names, addresses, phone numbers, credit card numbers, and other sensitive information could be buried inside natural language.

Most healthcare AI systems rely on Retrieval-Augmented Generation (RAG) and vector databases to search millions of insurance claims, physician notes, lab reports, billing documents, and customer records. But what happens before those documents become searchable? In this Short, we explain how embedding models convert raw healthcare documents into vectors—and why patient names, medical record numbers (MRNs), diagnoses, and Social Security numbers can end up stored inside your vector database if data isn't protected before ingestion.