How we built a PII masking layer for LLM APIs — local detection, reversible tokens, one line to integrate
The article discusses the development of Armos, a PII masking layer designed for LLM APIs. It allows for local detection and reversible tokenization of sensitive data, ensuring that real values never leave the server. This solution addresses privacy concerns while maintaining functionality for applications using LLMs like OpenAI and Anthropic.
- ▪Armos acts as a privacy boundary between applications and LLM APIs, preventing sensitive data from being transmitted externally.
- ▪The system uses local detection and replaces detected entities with deterministic tokens, which can be restored after the LLM processes the data.
- ▪Armos integrates seamlessly into existing code with minimal changes, ensuring that downstream processes remain unaffected.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 236530) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Dhroov Gupta Posted on May 25 How we built a PII masking layer for LLM APIs — local detection, reversible tokens, one line to integrate #ai #privacy #python #opensource 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.
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