AI adoption is expanding, but the workloads with the highest value — those spanning multiple teams, organizations, or regions — remain difficult to run. The core issues are practical, not theoretical: parties can’t share what they need to share, results can’t be independently verified, workloads get locked into specific providers, and nothing behaves consistently across environments. These limitations prevent many collaborative or regulated AI use cases from ever leaving the planning stage.
Super Protocol is a confidential-computing platform that lets organizations run AI workloads securely on any infrastructure while keeping data, models, and code protected during execution. It provides hardware-enforced isolation, cryptographic verification of all operations, and a neutral, cloud-agnostic architecture that behaves consistently across environments and partners.
Confidentiality:
Super isolates data inside hardware-secure enclaves that no administrator, operator, or cloud provider can access, encrypts every user interaction with keys visible only to the user, and enables multi-party computation where each participant’s raw information remains hidden from everyone else.
Verifiability:
Super provides cryptographic proof of how each workload ran, allows parties to confirm results independently of the cloud provider, and generates immutable execution records that meet audit and compliance requirements.
Neutrality:
Super runs on any cloud without vendor lock-in, is built on an open and transparent foundation, and keeps organizations in full control of their infrastructure, data, and keys.
Integrity:
Super applies the same security and execution standards across all environments, enforces consistent governance and deployment policies, and supports a scalable ecosystem where workloads run reliably from a single node to global multi-cloud networks. Together, these capabilities allow organizations to run sensitive AI, protect proprietary models and data, and collaborate across departments or companies without exposing underlying assets.
The technical foundation for confidential and verifiable execution has only recently become practical. Modern CPUs and GPUs now include isolation and attestation features that allow workloads to be protected during runtime, which was not broadly available in previous hardware generations. At the same time, organizations increasingly operate across multiple clouds, regions, and partners, creating environments where a single, provider-controlled trust model is no longer sufficient. Distributed systems, cryptographic identity frameworks, and verifiable execution methods have matured to the point where they can be combined into a coherent architecture rather than used in isolation. AI models and datasets have also become materially more sensitive and subject to stricter regulatory handling, making execution guarantees more important in day-to-day operations. These developments, taken together, create the conditions in which a platform like Super Protocol can be implemented and meaningfully applied.
Super Protocol is used in scenarios where AI workloads involve sensitive assets, multiple participants, or environments that require stronger guarantees than traditional cloud execution can provide. The most common applications fall into three categories:
