The current bottleneck in the AI industry is not actually computing power; what truly troubles people is data compliance. The EU's AI Act mandates that model training data must be traceable, while in the US, copyright lawsuits are emerging one after another. Domestically, the requirements for data usage regulations are becoming increasingly strict. However, existing storage solutions all have fatal flaws. Centralized storage sounds impressive with guarantees of data compliance, but how can others trust you? Decentralized storage, while transparent and open, does a poor job of protecting privacy.

Looking at Walrus's Seal feature, this is the real idea. It's not just about file encryption; instead, it has built a complete programmable access control system. Coupled with Sui's smart contracts, it can achieve data management that both preserves privacy and allows for traceability. For AI training data needs, this directly addresses the pain point.

Seal's approach lies in threshold key management. Traditional encryption schemes face two dead ends: either you manage the keys yourself and risk losing them, or you entrust them to a third party and feel uneasy. Seal's method disperses the key across multiple nodes, with each node only holding a part. Decryption requires reaching a set threshold. For example, 10 nodes must agree with at least 6 to decrypt, ensuring security while avoiding single points of failure.

Even more clever is that decryption permissions are programmable. You can write a smart contract on Sui to define decryption conditions: only users holding a specific NFT can access, or only during certain time periods, or require multi-party signatures for decryption. All these logic operations are executed on-chain, making them transparent and auditable. This should be a good solution for AI data that needs to meet various compliance requirements.