Security compliance makes it impossible for us to use public cloud infrastructure for model hosting. Meanwhile, dedicated secure AI infrastructure startups are printing money by offering isolated, sovereign sovereign environments. Can modern cloud technology deliver private, secure computing zones that scale dynamically without exposing proprietary corporate data?
3 answers
Confidentially computing via hardware-isolated enclaves is the primary way public cloud architectures are addressing these enterprise privacy concerns. These secure environments encrypt data directly within the CPU and memory during execution, meaning neither the cloud provider nor malicious actors can inspect the training workloads. This allows regulated industries like banking and healthcare to leverage massive public resource pools dynamically without violating data sovereignty laws, effectively challenging the niche platforms that rely solely on physically isolated servers.
What specific compliance certifications are your auditors demanding before approving a public deployment? Sometimes localized encryption setups are enough to satisfy the requirements.
Hardware enclaves provide excellent protection for real-time inference, but setting them up for massive distributed cluster training introduces significant performance overhead.
That performance penalty is exactly why many enterprise teams still prefer using completely dedicated, physically isolated bare-metal servers for their primary training runs.
We absolutely require strict ISO compliance along with full cryptographic data isolation at the hypervisor level before we can process any consumer telemetry data.