The problem

An AI developer needs to train or fine-tune a model on sensitive data owned by another company — for example, a hospital, a bank, or a government agency.

Normally this requires either sending the data to the AI provider (which breaks compliance and privacy laws) or sending the model to the data owner’s closed environment (which limits scalability and transparency). Both sides lose control: one risks exposing private data, the other risks leaking valuable IP.

How it works with Super Protocol

Super provides a confidential execution environment based on Trusted Execution Environments (TEEs) — isolated hardware-secured enclaves that guarantee privacy and integrity of the process.

Here’s the flow:

1. The AI model is encrypted and sent into the TEE.
    
2. The data stays on the data owner’s infrastructure (or another verified node) but is fed into the same enclave securely.
    
3. The model trains on the data inside this encrypted boundary — where neither side can see or copy the other’s assets.
    
4. The enclave produces attestation proofs — cryptographic evidence showing exactly what code ran, where, and that it remained confidential throughout.
    
5. Only the trained weights or results leave the enclave, while raw data and code remain sealed.

The outcome

• The data owner retains full compliance and never exposes raw data.
   
• The AI owner keeps the model confidential and gains access to learning signals safely.
   
• Both parties can audit the process through verifiable attestation.

Essentially, Super enables data-to-model collaboration without trust — allowing secure AI training across organizational and jurisdictional boundaries, with legal-grade proof of confidentiality and integrity