The AI Trust Gap: We're Auditing Outputs, But Nobody's Watching the Input

Hi all, Manjish here, founder of Pryvasee.AI.

I want to open a conversation rather than make a pitch, because I think this is a problem bigger than any one company can solve, and I'd like to hear how others in this community are approaching it.

Over the past year, watching enterprises adopt LLMs like ChatGPT, Claude, Gemini, Grok, DeepSeek, etc one pattern kept showing up. Almost every governance conversation started after the prompt was sent. Teams were building dashboards to review AI outputs, running periodic audits, writing acceptable use policies. All useful, but all reactive. Nobody I spoke to had a clear answer to a much simpler question: what actually happens to the sensitive data in that prompt in the moments before it leaves your organisation's control?

That gap is why we built Pryvasee.AI differently. Instead of sitting after the model and reviewing what came back, we sit before it. Pryvasee Guard screens prompts, documents, and images for PII, PHI, and PCI and other such sensitive data before anything reaches a model. Pryvasee Thread lets you run the same request across OpenAI, Gemini, Grok, and DeepSeek from one interface, so you're never trusting a single model's answer by default. And the Trust Engine scores every response that comes back for groundedness and hallucination risk, so there's a number behind "does this look right" instead of a gut feeling.

We built it natively on Azure (AKS, Azure SQL, Azure SQL Ledger for a tamper evident audit trail) because we think the next wave of AI governance problems won't just be about data leakage. They will be about proving, after the fact, exactly what happened, for a regulator, an auditor, or your own board. Most organisations can't do that today for a single AI interaction, let alone thousands a day across four different model providers.

We're early. MVP/Beta since July 2026, live on the Microsoft Commercial Marketplace since late August, and currently working through a handful of enterprise pilots rather than claiming a long customer list. I would rather be upfront about that than oversell it.

What I'm genuinely curious about: for those of you building or advising on enterprise AI adoption, is anyone handling the "before the model" problem today, whether with tooling, policy, or something else? And do you think this becomes a bigger issue as more employees start using multiple AI tools side by side, or does it resolve itself as the big model providers add more guardrails natively?

Would love to hear how others are thinking about this.