AI copyright and data compliance issues are becoming increasingly urgent. Recently, the open-source large model community LAION updated its data collection standards, which include an interesting move — they require contributors to store training datasets on Walrus Cipher Vault and publicly provide zero-knowledge proofs for the data.

However, this proof does not directly verify the data content, but rather confirms that "this dataset complies with copyright clearance and privacy de-identification rules." In other words, Walrus acts as an endorsement of the data's trustworthiness.

This is significant. Any research institution or enterprise downloading and using these models can verify on-chain whether the data source is compliant, directly reducing legal risks. For organizations like LAION, this is a more robust approach in the era of rapid growth of large models.

From Walrus's perspective, this addresses the most urgent compliance needs in the explosive AI industry. Although individual datasets are large but infrequently accessed, the strategic importance is immense — it can attract top research institutions and tech companies for practical applications. In this scenario, tokens become a payment tool for purchasing "data ethics insurance," gradually demonstrating their ecological value.