In this episode, Joseph Perla, founder and CEO at TrustedRouter, explains how his company's confidential-computing-based LLM router lets teams send prompts to hundreds of AI models while cryptographically verifying, rather than simply trusting, that no one, including TrustedRouter itself, can see the data passing through. Joining him are TC Niedzialkowski, former head of IT & security at Opendoor, and Keith McCartney, svp of security and IT at DNAnexus.
Want to know:
- Why hasn't the AI privacy problem already been solved, given how many tools and architectures already exist?
- What's wrong with the "zero data retention" promises most AI providers offer today?
- How does a confidential virtual machine let a company verify, instead of just trust, that its own router isn't looking at its prompts?
- For a use case like HIPAA-covered medical data, does TrustedRouter provide one complete, auditable path from a user's machine to the model and back?
- How does remote attestation confirm end-to-end encryption down to the chip level?
- Does TrustedRouter's SOC 2 Type 2 attestation cover the AI workload itself, or just its own SaaS interface?
- Can customers restrict which data centers or jurisdictions their AI workloads run in?
- What happens to a company's existing AI usage monitoring once it starts routing through TrustedRouter?
Check out the episode for the answers you need.
Huge thanks to our sponsor, TrustedRouter
TrustedRouter is the open-source AI gateway for teams that need privacy with proof. It routes hundreds of models through attested confidential compute, keeps prompts and outputs out of logs, verifies the code handling each request, and adds provider failover, spend controls, and OpenAI-compatible APIs for production AI systems.
