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Pseudonymizing sensitive data for LLMs without losing context

Pseudonymizing sensitive data for LLMs without losing context

by n00pn00p·Apr 15, 2026·4 points·8 comments

AI Analysis

●●SolidBig BrainSolve My Problem

Proxy pseudonymizes sensitive data so LLMs don't hallucinate like they do with regex.

Strengths
  • Prevents LLM syntax hallucinations by keeping pseudonyms structurally valid.
  • Transparent proxy architecture means zero changes to existing agent prompts.
  • Open-sourcing the DLP layer builds trust for security-sensitive deployments.
Weaknesses
  • Blog post focuses heavily on selling the broader Ghost Analyst SaaS platform.
  • Unclear if the open-source proxy supports providers beyond Anthropic's Claude.
Category
Target Audience

Security engineers building AI agents on sensitive data

Similar To

Microsoft Purview · LLM Guard · Protect AI

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AI/ML●●Solid

I built proxy that keeps RAG working while hiding PII

Consistent pseudonymization beats redaction when RAG embeddings must survive.

Big BrainSolve My Problem
rohansx
403mo ago