Data privacy has become a core concern in the AI era. Currently, massive amounts of information are stored in various platforms, and users often feel helpless regarding data security. Recently, I came across the Walrus protocol, which offers a different approach.

Compared to traditional reliance on a single server, Walrus adopts a globally distributed network storage. Your files are no longer concentrated in the hands of a single institution but are distributed across multiple locations, fundamentally reducing single-point risks. Coupled with sophisticated encoding technology, it ensures fast data access while avoiding the drawbacks of centralized storage. In practical use, read latency is not significantly noticeable.

The Seal module is a standout feature of this system. It allows users to control who can access which data with fine granularity, similar to setting a safe. Sensitive information such as medical records and financial documents remain visible only to authorized persons, even when stored online. This is especially critical for privacy-related application scenarios.

Walrus has received strategic support from the Sui ecosystem. As a native storage layer, it can deeply integrate with the ecosystem, making development paths relatively clear. From the application side, an increasing number of AI projects and digital creators are choosing to connect, indicating that this solution truly addresses market demand.

In terms of security, it employs multiple technologies to ensure long-term data availability, which is vital for any storage system.