Serve your models on confidential GPUs in your own cloud. The model, the data and every prompt are decrypted only inside an enclave that has proven what it is, and every user can verify it from a browser before sending a prompt.
The organizations with the most valuable data, in government, defense, finance and healthcare, are also the ones that can’t let it cross a security boundary. Model owners, in turn, won’t place their weights on infrastructure someone else administers. Confidential computing closes that gap by protecting data and models while they are being processed, not only at rest and in transit.
Duality Technologies runs on the confidential computing you already use, on AWS, Microsoft Azure, Google Cloud, on premises or in air-gapped networks. On top of it, Duality adds the controls that turn a protected environment into a governed workflow.
Before any data or model is released, the owner’s node checks the enclave’s attestation and the digest of the workload it approved. If any check fails, the key is never sent.
Data owners and model owners each encrypt under their own key. Duality adds no key store of its own, so a data owner and a model owner can work in the same session without either seeing the other’s asset.
Policies set which workloads can use an asset, by whom, how many times and until when. Every key release and policy decision is recorded, and the record carries metadata only.
Serve a proprietary LLM on confidential GPUs in your own cloud account. The model owner decides what runs, the operator manages the service without seeing content, and every user can verify the enclave from a browser before sending a prompt.
Test a vendor’s AI model on your own regulated data inside a confidential environment. The vendor never sees your records, and you never see the model’s weights.
How attestation-gated key release, post-quantum key exchange and confidential GPUs fit together.
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You may not need to, for a single workload in one cloud run by a team with enclave expertise. You need more when you want key control for each party, policies, audit, or the same workflow across several clouds and partners. Duality runs on your provider’s confidential computing and adds those layers.
Not necessarily. With a TEE, data travels encrypted into the enclave and is decrypted only inside it, after attestation. Where data can’t leave its site, federated learning sends the computation to the data instead.
No technology does that on its own. Confidential computing supports requirements to protect data in use and produces evidence for audits. Compliance decisions stay with your legal and compliance teams.