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R

Redacting Indian personal data in R before it reaches an LLM

R users are wiring language models into their work quickly: summarising call notes with ellmer, classifying tickets, drafting replies. In a bank, an insurer or a hospital those notes are full of Aadhaar numbers, PANs and mobile numbers, and the DPDP Act, RBI guidance and plain caution all say they should not go to a model that does not need them - often not even to one running in-house on a shared server.

credgate is an R package (with a Python twin) that finds Indian personal data and takes it out before the prompt leaves your session. This post shows the R side.

A Shiny app for InfoSec: which models may see which data

Once a bank lets analysts use language models, someone has to decide which models may see what. Customer PANs to the in-house model on the analyst's laptop: probably fine. To a shared GPU server: perhaps not. To an external provider: no. A card number with its CVV: never. That someone is usually InfoSec or compliance, and they should not have to edit YAML to say it.

credgate keeps these decisions in a policy, and its R package includes a Shiny app to edit one. This post walks through it.