If you're building LLM features on top of OpenAI or Anthropic, you're almost certainly sending raw user data to a third-party model provider. Names, emails, phone numbers, tax IDs, health records — whatever your users type, it goes straight to the API.
Here's the uncomfortable part: every attempt to fix this problem seems to make it worse. The most obvious fix — sending your text to a cloud anonymisation service first — means you're solving a data privacy problem by sending your sensitive data to another third party.
I was talking to a healthtech team recently that had been blocked from using GPT-4 for clinical notes for months. Not because the engineers didn't want to — they did. Legal wouldn't sign off because every API call meant patient data leaving their infrastructure. The problem wasn't capability. It was the missing privacy boundary between their data and the LLM.
Armos is that boundary. A local detection and masking layer that sits between your application and the LLM API — PII never leaves your server, and real values are restored in the response automatically.
This is how it works under the hood.
