AI on Confidential Compute on Azure
Run the AI OS on Azure confidential computing — your data, models, and prompts stay encrypted even while in use, protected from the cloud operator and infrastructure administrators by hardware-level isolation.
Encrypted In Use
Data and models stay encrypted in memory via AMD SEV-SNP and Intel TDX — not just at rest and in transit.
Confidential GPUs
Run AI inference and training on confidential GPU VMs, with the GPU's memory protected inside the trust boundary.
Remote Attestation
Cryptographic proof a workload runs in a genuine, untampered TEE before any secret or model is released to it.
AI on confidential compute on Azure means running AI workloads inside hardware-based Trusted Execution Environments — Azure confidential VMs and GPUs built on AMD SEV-SNP and Intel TDX — so data and models stay encrypted in memory during processing, shielded even from Microsoft and privileged administrators, with remote attestation proving the environment's integrity.
Cloud encryption usually protects data at rest and in transit but leaves it exposed in memory while it is being used. Sovereign confidential computing closes that gap. Running the AI OS on Azure's confidential VMs and GPUs lets you reach hyperscale capacity for AI without surrendering confidentiality of your most sensitive data and models.
Confidential Compute on Azure in the Scrydon platform
One integrated, sovereign architecture. Here is where Confidential Compute on Azure sits — highlighted against the full stack it works with.
The AI OS for Humans & AI Agents
Ontology & Semantic Layer, one connected model for your data, knowledge & processes
Combining the best of data lakes, data warehouses and search
AI agents, workflows & automations that execute across your systems
Integrate across A2A, MCP, legacy systems and data sources
Secure domain federation, trusted data sharing, and cross-boundary intelligence
Sovereign Foundations
Confidential Compute on Azure in depth
The AI OS only works if it can be trusted. Every layer of the platform rests on a zero-trust infrastructure and identity foundation that operates consistently from fully air-gapped on-premises deployments through to hyperscale cloud environments. Sovereignty is not a feature added on top — it is the condition under which everything else operates.
- Zero-trust architecture: Continuous verification for every request, every user, and every workload — no implicit trust, even inside the perimeter.
- Federated identity: Seamless integration with your existing IdP (SAML, OAuth 2.0, OIDC) for unified, policy-enforced access control.
- Air-gapped deployment: Run the complete platform with no external network dependencies — ideal for defence, critical national infrastructure, and classified workloads.
- Confidential computing: Hardware-level encryption of data in use via AMD SEV-SNP and Intel SGX, protecting workloads even from infrastructure administrators.
Deployment Options: From Air-gapped to Cloud
Deploy the Scrydon platform where it makes sense for you — from air-gapped environments to public cloud — with sovereignty, compliance, and auditability built in.
No data leaves your jurisdiction. No black-box AI. No compromises on control.
This is sovereignty by design.
AI workloads inside a Trusted Execution Environment
The AI OS deploys onto Azure confidential computing so that the entire AI workload — data, model weights, and prompts — is processed inside hardware-isolated enclaves. Keys and secrets are released only after the environment proves its integrity through attestation.
Confidential VMs — Hardware-isolated VMs on AMD SEV-SNP and Intel TDX keep memory encrypted during execution.
Confidential GPUs — GPU-accelerated inference and training with the accelerator inside the confidential trust boundary.
Remote attestation — Azure Attestation verifies the TEE before secrets, keys, or models are provisioned.
Sovereign key management — You hold the keys; Microsoft and administrators cannot read data in use.
Hyperscale capacity without surrendering confidentiality
Regulated and sovereignty-conscious organisations often need cloud-scale AI but cannot expose sensitive data to the cloud operator. Confidential computing on Azure resolves the tension: you get hyperscale elasticity and GPU availability while the data and models remain cryptographically protected from the platform itself — the same zero-trust posture the AI OS applies everywhere else.
Microsoft Fabric, Databricks, and Foundry do not keep your data confidential end to end. Our solution does.
The mainstream Azure analytics and AI platforms — Microsoft Fabric, Databricks, and Azure AI Foundry — do not keep your data encrypted in use end to end. Fabric and Azure AI Foundry process data in standard, non-confidential compute. Azure Databricks can run its classic compute clusters on AMD SEV-SNP confidential VMs, but that option covers the compute plane only: the control plane, which holds notebooks, query plans, job results and cluster logs, runs in Databricks' own account on standard compute. The AI OS runs the same class of analytics and AI workloads inside hardware Trusted Execution Environments end to end, so your data, models, and prompts stay encrypted in use and out of reach of Microsoft and privileged administrators.
Fabric, Databricks, Foundry — Fabric and Foundry run on standard compute. Databricks can protect its worker nodes, but never its control plane — so data is decrypted in memory somewhere on the path either way.
The AI OS — Runs on Azure confidential VMs and GPUs — data and models stay encrypted in use, protected from the cloud operator by hardware isolation.
Frequently asked questions
How can I run AI and data on Azure Confidential Compute?+
Do Microsoft Fabric, Databricks, and Azure AI Foundry run on confidential compute?+
What is confidential computing on Azure?+
Can AI workloads use confidential GPUs on Azure?+
Is data protected from Microsoft and cloud administrators?+
What is remote attestation?+
Why run sovereign AI on a hyperscaler at all?+
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