Kian Vance

Architecture, not policies, protects privileged information during AI inference.

Five controls enterprises need to enforce data sovereignty in AI deployments before signing.

Seven safeguards determine whether AI tools can safely handle confidential professional data.

Opt-out policies let companies train on your data first and ask permission later.

Privacy policies let companies change data uses; training guarantees would lock them in.

Where your prompt goes determines whether professional privilege survives.

Each model trades your data differently: training, ads, platform tracking, or government access.

A five-step walkthrough of how admission controllers verify attestations cryptographically.

You can add and multiply encrypted data, but only to a fixed depth before noise makes it unreadable.

Privacy policies promise protection that payment doesn't actually deliver or guarantee.

Regulation forced healthcare and finance to adopt federated learning.

Vectors marked deleted stay recoverable and searchable inside production databases.

Encryption protects data between GPUs, but the performance cost on current hardware is steep.

Attestation and key release, not model loading, drive confidential inference cold starts.

Trusted hardware lets hospitals and AI firms compute together without exposing secrets.

Hardware-sealed enclaves protect LLM prompts from cloud operators and hypervisors during inference.

Cryptographic verification for AI models fills the gap between documentation and actual artifacts.

Federal agencies are rushing to deploy AI faster than their security infrastructure can support it.

Architectural controls must protect PHI throughout inference, not just at the perimeter.

Math-enforced privacy lets AI models process sensitive data without ever seeing it.

How local differential privacy lets devices add noise before transmitting data.

Differential privacy in LLM fine-tuning trades learning signal for formal privacy guarantees.

Confidential computing alone cannot prevent all attacks on encrypted LLM inference.

Four structural risks in cloud AI systems threaten journalists' source protection.