Walrus protocol's recent performance on the Sui network is indeed worth paying attention to. As a data platform focused on the AI era, it addresses a core pain point: how to make data truly trustworthy and traceable?

The core logic is straightforward. Walrus assigns a unique identifier to each piece of data and model, and the entire update process can be verified. In other words, data is no longer a black box but has a complete trust record from the source. This is crucial for AI applications—garbage data leads to garbage outputs. Walrus's mechanism directly avoids this problem. It also supports data permission control and secure sharing, allowing AI agents to access data in real-time, and enterprises to build fully auditable AI workflows.

The combination of Walrus + Sui is the highlight. Sui acts as the coordination layer and source layer, responsible for executing access rules and recording verifiable receipts. Integrating Walrus's data storage, Seal's programmable encryption and access control, and Nautilus's confidential verifiable computing creates a composable infrastructure. This enables the construction of a complete verifiable AI process: data stored on Walrus, securely processed in Nautilus, with Sui responsible for coordination and recording.

In essence, this approach uses blockchain and cryptography concepts to fundamentally solve the data trust issue for AI applications. The WAL token has also gained more attention because of this.