Super Protocol provides an open‑source execution layer that runs AI workloads inside hardware‑secured enclaves across any cloud or on‑premise environment. It encrypts each session with user‑controlled keys and generates cryptographic attestations, enabling multiple parties to collaborate on models and data without exposing raw assets while meeting regulatory compliance. The platform offers cloud‑agnostic SDKs and an AI marketplace for secure model deployment and audit‑ready reporting.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and research institutions cannot safely collaborate on high‑value AI workloads because sharing raw datasets or proprietary models exposes sensitive information, and traditional cloud execution lacks hardware‑level isolation and verifiable proof of computation. This limits joint training, cross‑organization analytics, and deployment of regulated AI applications. Additionally, vendor‑specific runtimes create lock‑in and inconsistent security guarantees across multi‑cloud environments.
Solution
Super Protocol delivers an open‑source, neutral execution layer that runs AI workloads inside hardware‑secured enclaves (TEE) on any cloud or on‑premise infrastructure. Each user interaction is sealed with a per‑session cryptographic key, ensuring end‑to‑end confidentiality of prompts, inputs, and outputs. The platform generates cryptographic attestation and immutable execution logs, allowing all participants to independently verify that the computation adhered to policy and compliance requirements. Multi‑party compute enables multiple owners to contribute models or data to a shared enclave without ever revealing their raw assets, supporting collaborative training and joint inference. By abstracting the enclave management and providing a cloud‑agnostic runtime, Super Protocol eliminates vendor lock‑in while maintaining consistent security and governance across heterogeneous environments. The solution integrates with an AI marketplace and offers SDKs and APIs for seamless model deployment, monitoring, and audit‑ready reporting.
Target Audience
Primary customers are regulated enterprises—such as financial services, healthcare systems, and government agencies—and AI platform providers that require secure multi‑tenant AI workloads, collaborative model training, and verifiable compliance across multi‑cloud deployments.
Features
- Hardware‑rooted Trusted Execution Environments (Intel SGX, AMD SEV, NVIDIA Confidential Computing) that isolate memory and CPU state from the host OS, hypervisor, and cloud operator.
- Per‑session end‑to‑end encryption with user‑controlled keys, preventing any intermediate logging or storage from exposing data.
- Multi‑party confidential compute framework that securely loads disparate models and datasets into a single enclave, producing only agreed‑upon outputs.
- Cryptographic attestation and tamper‑evident execution receipts that can be audited by regulators or business partners.
- Cloud‑agnostic runtime and open‑source SDKs (Python, Rust, Java) for model packaging, deployment, and lifecycle management across public, private, and hybrid clouds.
- Integrated AI marketplace and API layer for discovering, licensing, and invoking confidential models while preserving provenance and usage policies.
- Built‑in compliance modules (HIPAA, GDPR, FedRAMP) that map enclave attestations to regulatory audit trails.