Perspectives on running commercial LLMs over sensitive data — without ever exposing it. Product updates, technical deep dives, and lessons from working with security and compliance teams.
AI risk is two different perils wearing one name. Output risk is what the model says. Exposure risk is what the model sees. One is probabilistic. The other is set by architecture, and it can be bounded.
Read more →Accounting firms can use AI on client financials without exposing them. Learn which tasks are safe for public AI, which are not, and how confidential AI lets firms analyze client data the model never sees.
Read more →Short answer: not on the public version, and enterprise versions still see your plaintext. What the AICPA rule and IRC 7216 actually say, and how firms run AI on client data without exposing it.
Read more →Enterprises want to use AI on their most valuable data, but privacy, compliance, and trust concerns often stand in the way. In this article, we explain the design decisions behind Nera ChatApp and how it enables AI on encrypted data.
Read more →A practical guide for enterprise data and technology leaders on how to use frontier AI models on sensitive data without your data ever leaving your controlled environment.
Read more →HIPAA governs how AI tools can process patient data and most enterprise AI tools don't meet the bar. A practical guide for healthcare technology and compliance leaders on what's allowed, what isn't, and how to use AI on PHI without compliance exposure.
Read more →Confidential AI protects enterprise data during computation — not just at rest or in transit. Learn what it is, how it works, and why regulated industries like healthcare and finance need it now.
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