Sui ecosystem's decentralized storage infrastructure Walrus is becoming a new benchmark for data management in the AI era. Rather than calling it a "cloud hard drive," it's better to say it equips AI data with a "authenticity verifier"—proving data integrity and availability through on-chain event verification, cutting off the possibility of poisoning and tampering at the source.

Coupled with Seal encryption layer and flexible access control, data can be encrypted and stored, time-locked, and support paid unlocking. This combination is practically tailor-made for applications like AI training data, proxy memory, and model weights. By 2025 to 2026, many projects are already adopting it.

**Talus Labs Case: Enabling AI Agents to Have Reliable "Long-Term Memory"**

Talus is currently the most attention-grabbing on-chain autonomous AI agent framework. Its architecture is straightforward—Sui handles workflow coordination and execution, while Walrus specializes in data layer (training sets, context, memory banks, model files).

Why choose Walrus? Simply put, AI agents need to pull massive files in seconds—market sentiment data, DeFi protocol snapshots, social media trends, and more. The old problems with cloud storage are data loss, lack of auditability, and inability to prevent deletion. Walrus uses RedStuff erasure coding combined with on-chain verifiable proofs to ensure data is always online and traceable at any time. For AI agents running 24/7, this is a lifeline.

**CUDIS Wellness: A New Approach to User Data Ownership and AI Monetization**

CUDIS collects health data via wearable devices (like CUDIS Ring) and then uses AI models to extend lifespan. But the traditional model has a big flaw: the data owner is the user, but the company profits.

CUDIS aims to flip this logic. Users' health data is stored on Walrus, encrypted, controllable, and the user holds the keys. When training models with this data, users explicitly authorize—tracking how many times the data is used and how much revenue is generated. Walrus's programmable access control makes this feasible: time locks (data accessible only during specific periods), payment conditions (using data requires payment), audit logs (who used it, for how long, all transparent). In this way, users can enjoy AI-driven health services and benefit from the value of their own data.