The Sui Foundation has introduced a comprehensive infrastructure stack designed to bring verifiability, access control and provenance to AI workflows—from data to inference. The announcement positions the Sui Stack as the “trust layer” for the next generation of intelligent systems.
At the heart of the new architecture lie four core components: Walrus enables trustworthy, monetisable data markets; Seal provides programmable encryption and access control; Nautilus offers confidential, verifiable compute within trusted enclaves; and the underlying Sui blockchain acts as the coordination and provenance layer.
Together, they deliver an end-to-end infrastructure in which data, compute and access rules are auditable and tamper-resistant. In practice, developers can encrypt a dataset on Walrus, define access policies via Seal, run model inference inside Nautilus with cryptographic proof of correctness, and record the entire workflow on Sui with immutable receipts.
This shift matters because many AI systems today operate in opaque silos: training data, inference logic and model behaviour remain hidden, eroding trust and accountability. Here, the stack tackles three major friction points: provenance (knowing what data was used), access (who can use it and how) and compute integrity (whether a model ran as claimed).
Building AI that proves its work, not just claims it
For end users, this means recommendations, predictions or decisions made by AI can come backed by verifiable evidence—not just assertions. From enterprise analytics to collaborative data-rooms and AI marketplaces, the use cases are broad: encrypted data sharing between organisations, token-gated model access, and auditable pipelines where each step issues a proof of what happened.
That said, executing such a stack at scale presents challenges: integrating legacy systems, ensuring performance overhead stays low, and educating stakeholders about the value of cryptographic audit trails. Nonetheless, the launch of this stack signals a paradigm shift: AI is not just about bigger models, it’s increasingly about infrastructure that enables trust. With the Sui Stack, the era of verifiable intelligence is stepping out of theory and into production.
