We've been building a middleware layer that acts like a firewall for LLMs, it sits between the user and the model (OpenAI, Claude, Gemini, etc.) and intercepts prompts and responses in real time.<p>It blocks prompt injection, flags hallucinations, masks PII, and adds logging + metadata tagging for compliance and audit.<p>But we’re hitting the classic startup blind spot: we don’t want to build in a vacuum.<p>What do <i>you</i> feel is still broken or missing when it comes to:
- Securing LLM prompts/responses?
- Making GenAI safe for enterprise use?
- Auditing what the AI actually said or saw?<p>We’d love your feedback — especially if you’re working on or thinking about GenAI in production settings.<p>Thanks!
One big gap I see is context-aware filtering and memory control.<p>Many tools block clear prompt injections, but few detect contextual misuse. This happens when users gradually direct the model over many sessions or subtly draw out its internal logic.<p>Your middleware sounds promising; I'm excited to see where it goes.