@Walrus 🦭/acc
The integration of Walrus and Seal marks a massive shift in how we handle sensitive information for machine learning. This architecture ensures that privacy remains intact throughout the entire lifecycle of an AI pipeline.
Data is encrypted at the source before being stored as protected blobs on Walrus. When a training job begins Seal fetches specific encrypted segments into a secure enclave where decryption happens only for active computation. Because gradients are re-encrypted before leaving the environment the system eliminates the need for a centralized trusted operator.
This decentralized approach removes the traditional trade-off between data utility and user security. Developers can now build powerful models while guaranteeing that raw data remains inaccessible to third parties at all times.
