How to pseudonymize documents so AI can process them without exposing personal data

Companies using AI assistants routinely share sensitive documents — including contracts, invoices, and HR emails — that contain personal data and trade secrets, raising serious GDPR and data-protection concerns. Simply redacting information renders documents useless for AI reasoning, so a technique called pseudonymization replaces sensitive values with realistic surrogates using a locally stored mapping. The approach uses three detection layers: pattern matching for structured data like IBANs and emails, named-entity recognition for unstructured identifiers like names and organizations, and custom exclusion lists to reduce noise. Crucially, the mapping file never leaves the local machine, and the AI's output can be reverse-substituted to restore all original values exactly. This allows organizations to gain AI productivity benefits while keeping identifiable and commercially sensitive information off third-party infrastructure.