Kian Vance

Soft-delete alone won't prove a user's data left your vector database.

Encrypting multi-GPU training data costs speed; here's where and how much.

Six sequential layers, not one bottleneck, determine confidential inference cold start cost.

TEEs offer the only production-speed path to private multi-party inference.

TEEs encrypt prompts inside enclaves, closing the gap TLS leaves behind.

Cryptographically binding AI models to their provenance without reinventing supply chain security.

Federal agencies are racing to deploy AI faster than their infrastructure was built to handle it.

Architectural controls must protect PHI inside AI pipelines, not just around them.

Math-based protocols let AI models process data without ever seeing it in plain form.

Privacy is added on devices before data leaves, making breaches irrelevant.

Differential privacy reduces model leakage but degrades accuracy at scale.

Five trust boundaries structure risk in confidential LLM inference deployments.

How cloud AI tools routinely expose journalists' sensitive sources to legal demands and breaches.