AI on Azure
Run the AI OS on Microsoft Azure with sovereignty controls — EU data residency, customer-held keys, and your own perimeter — so you get hyperscale capacity without handing over control of your data, models, or jurisdiction.
Hyperscale Capacity
Reach global Azure capacity and GPU availability for AI at scale, without standing up your own datacentre.
Data Residency
Choose EU regions and align with the Microsoft EU Data Boundary to keep data resident where your rules require.
Your Keys, Your Perimeter
Hold your own encryption keys (BYOK/HYOK) and enforce zero-trust access, so control stays with you on a global cloud.
Sovereign AI on Azure means running the AI OS on Microsoft Azure as a cloud deployment target while keeping sovereignty controls in place: EU data residency aligned with the Microsoft EU Data Boundary, customer-held encryption keys (BYOK/HYOK), zero-trust access, and confidential computing available — so you reach hyperscale capacity while your data, models, and keys stay under your control.
Azure brings global, hyperscale capacity and GPU availability that few sovereign clouds can match. Running the AI OS on Azure lets you use that scale while keeping sovereignty controls in place: choose EU regions for data residency, hold your own encryption keys, and enforce the same zero-trust perimeter the platform applies everywhere. For workloads that must stay encrypted even while in use, the AI OS runs on Azure confidential computing — covered in depth on the Confidential Compute on Azure page.
AI on Azure in the Scrydon platform
One integrated, sovereign architecture. Here is where AI on Azure sits — highlighted against the full stack it works with.
The AI OS for Humans & AI Agents to enable your processes
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Link your processes, knowledge & data to ontologies.
Unified storage, structured compute, and secure multi-modal data processing.
Autonomous operatives with specialised skills executing tasks across systems.
Sovereign pipelines, federated APIs, and seamless connector meshes.
Secure domain federation, trusted data sharing, and cross-boundary intelligence.
AI 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.
Hyperscale capacity with sovereignty controls
Running the AI OS on Azure gives you cloud scale while keeping the controls that matter. Data residency, customer-held keys, and zero-trust access all stay in place, so reaching for hyperscale capacity never means handing Microsoft your data or your jurisdiction.
EU data residency — Deploy in EU regions and align with the Microsoft EU Data Boundary to keep data resident where your rules require.
Customer-held keys — Hold your own encryption keys with BYOK/HYOK, so your data and models cannot be read without your keys.
Zero-trust perimeter — The same federated identity and zero-trust access model applies on Azure as everywhere else the AI OS runs.
Same platform everywhere — The same AI OS, ontology, agents, and insights run on Azure as on-premises — move workloads without changing the security model.
Add confidential computing for data-in-use protection
Data residency and customer-held keys protect data at rest and in transit. When a workload must stay protected even while being processed — encrypted in memory, out of reach of Microsoft and privileged administrators — run it on Azure confidential computing. That depth is covered on the Confidential Compute on Azure page.
Confidential VMs and GPUs — Run AI inside Azure confidential VMs and GPUs (AMD SEV-SNP, Intel TDX) so data and models stay encrypted in use.
Remote attestation — Cryptographic proof the workload runs in a genuine TEE before any key, secret, or model is released to it.
Frequently asked questions
What does running the AI OS on Azure mean?+
Can I keep data resident in the EU on Azure?+
Who holds the encryption keys?+
How is this different from Confidential Compute on Azure?+
Why run sovereign AI on a hyperscaler at all?+
Explore the platform
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