Confidential Computing for Sovereign AI: The Emerging ...

Overview: Confidential computing systems involve computation over private inputs held by mutually distrusting parties while producing public, cryptographically verifiable evidence of what was computed. Trusted execution environments (TEEs) are a practical building block for these systems, enabling code to run inside hardware-encrypted enclaves that emit attestations binding a measurement of the loaded code to genuine hardware. We present Cove, a framework that composes attested computations into multi-sta

Key Topics: ge, multi-party workflows structured as directed acyclic graphs. Each node runs in its own enclave, decrypts private inputs only under attestation-gated key release, and emits a certificate that downstream nodes can require as a cryptographic precondition before consuming upstream artifacts; anyone holding the workflow bytes and the TEE vendor's attestation roots can verify the whole chain non-interactively. We show that a small set of reusable primitives - confidential artifact provisioning, encrypted dynamic outputs, attested ephemeral-key channels (RA-TLS), and certificate-gated preconditions - compose into systems that lift attestation fr

Takeaways: erties, and use this to express practical applications in secure AI systems including confidential AI inference, attested AI benchmarks, and training-code verification. We give an open-source reference implementation on Intel TDX via Phala Cloud's dstack and demonstrate end-to-end feasibility with an attested benchmark workflow.