PII Detection Before RAG Ingestion: Screening Documents in C#

Retrieval-augmented generation (RAG) has quickly become one of the most useful patterns in the burgeoning world of enterprise AI. Rather than fine-tuning a model on private data, a RAG system instead extracts text from internal documents, splits that text into chunks, turns each chunk into a vector embedding, and stores those vectors so a language model can fluidly pull the most relevant passages at question time. The payoff from a well-designed RAG system is considerable: employees can ask plain-language questions across wikis, contracts, support tickets, and policy files and get grounded, cited answers in reply — with no training involved.

That same convenience is what makes personally identifiable information (PII) particularly dangerous here. Feeding internal documents into an AI service means copying their contents into a new, searchable, and hard-to-purge form; if any of those documents contain Social Security numbers, bank account numbers, or health details, that nifty RAG system quietly becomes a fountain of regulatory and compliance violations.