Something that bothers me about current AI systems is how much data leaks with every single API call. Your prompts, your context, your entire business logic gets exposed to model providers. For serious applications handling sensitive data this is completely unacceptable.
Think about medical diagnosis systems. Patient data is incredibly sensitive but traditional AI requires sending all that data to a model provider. That creates massive compliance headaches. What we really need is inference without exposure.
Different approaches exist. Homomorphic encryption has insane overhead. What takes milliseconds normally could take minutes when encrypted. But combining ZK proofs with encrypted inference might hit a sweet spot between security and performance.
Keep reading
- Homomorphic AI Is Real
Computing on encrypted data without decrypting seems impossible. But it works for AI, slowly.
- Decentralized Training Actually Works
Training models across distributed computers is possible but expensive. It democratizes AI development.
- Privacy Tech Renaissance
ZK, MPC, FHE all maturing together. The privacy stack is becoming usable not just theoretically possible.