In cloud environments, data is encrypted at rest and in transit, but it must be decrypted during execution, where it becomes visible to the host system’s privileged layers. This exposure is not a theoretical flaw — it is the primary attack surface in modern cloud architecture and the reason confidential workloads in finance, healthcare, government, and high-value AI often cannot move to public clouds. Even with strong policies and access controls, the operating system, hypervisor, and administrative layers retain technical visibility into memory, which means sensitive data and models are never fully isolated during computation.
Super addresses this by running workloads inside hardware-secure enclaves that isolate execution from the surrounding system entirely. Data enters the enclave encrypted, is decrypted only inside the protected memory region, and never becomes accessible to the OS, hypervisor, cloud provider, or Super itself. The isolation is enforced at the silicon level, eliminating reliance on operator controls or infrastructure policies.
For clients, this means they can finally run their most sensitive workloads in untrusted environments without exposing raw data or models. Execution remains sealed end-to-end, unlocking cloud usage for regulated industries, proprietary AI models, and high-value datasets that previously required dedicated infrastructure.
User interactions with AI systems — prompts, inputs, outputs, and metadata — typically pass through multiple software layers where they can be logged, cached, or observed by operators. Even reputable cloud and model providers often store parts of interactions for debugging or analytics, creating potential exposure of private, regulated, or proprietary information.
Super prevents this by encrypting every session with a key visible only to the user and managed inside enclaves rather than on the server. Even if data is stored temporarily for performance or billing, it remains unreadable to administrators, platform operators, and model providers. No party other than the user has the ability to decrypt or reconstruct the interaction.
For clients, this ensures that sensitive prompts, customer information, strategic inputs, and model outputs remain confidential end-to-end. It enables internal teams to use AI systems safely, prevents inadvertent data leakage through logs, and supports strict confidentiality requirements across regulated and high-risk environments.
When organizations need to combine assets — a proprietary model from one party and sensitive data from another — traditional setups force someone to reveal their raw inputs. Legal agreements can restrict misuse, but technically, one side must still trust the other with something they cannot afford to expose.
Super solves this by allowing each participant to load their asset into the same enclave without revealing it to anyone else. The data owner contributes their dataset, the model owner contributes their model, and computation happens entirely inside the isolated environment. Only the agreed-upon output leaves the enclave, and neither side gains visibility into the other's raw asset.
For clients, this enables joint analytics, multi-institution AI training, cross-industry cooperation, and vendor collaborations that were previously blocked by confidentiality constraints. Sensitive institutions can now work together without sharing their underlying data or intellectual property — allowing new business models and new forms of secure interaction.
