Original Summary

I'm a final-year CS student. Over the last few months I built AegisFlow - an open-source proxy that sits between your app and OpenAI/Claude, strips PII from prompts before they leave your server, and rehydrates the real values in the response. SHA-256 signed audit log included. The technical stack: Fastify gateway → Presidio + DistilBERT NER for masking → Redis for ephemeral token maps → async Kafka audit trail → PostgreSQL. Benchmarked at 4ms p95 internal overhead at 200 RPS. GitHub: https://github.com/JhanviN/AegisFlow I built this after noticing that small AI startups either send raw user data to LLM APIs (risky) or manually strip PII with regex (fragile and inconsistent). Enterprise solutions like Private AI or Nightfall exist but start at $10K+/year and require a procurement cycle. I'm trying to understand if there's a real market for a self-serve hosted version at $29-99/month for teams that need to tell their enterprise customers "we handle PII safely" but don't have a security engineer on staff. Three honest questions: If you're building AI features that touch user PII, how are you handling it today? Would a shareable audit log export, a PDF you can hand to a client's security team be worth paying for? What would make you trust a tool like this enough to route production traffic through it? Not pitching. Genuinely trying to understand if I'm solving the right problem before I build the wrong product.   submitted by   /u/Expensive-Pop6687 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/16 21:43:59