As organizations apply AI to more business-critical and data-intensive workloads, AI processing is moving closer to where data is created, decisions are made, and operations take place. Manufacturers can analyze confidential production data next to the systems that generate it. Pharmaceutical companies can apply AI to clinical trials within controlled environments.
Government agencies can run AI applications on sensitive information while maintaining established security and operational boundaries. This shift reflects ongoing innovation across the technology ecosystem to give organizations greater flexibility in where they run AI and process sensitive data, and it is changing the digital sovereignty conversation. It now encompasses where data is stored and processed, where workloads run, who can access and operate the systems around them, and how those systems remain secure and operational as requirements change.
The next phase of digital sovereignty will take shape across the technology ecosystem, through closer alignment among silicon, infrastructure, cloud, security, data, and AI. Learn more about digital sovereignty As AI extends into more sensitive and mission-critical workloads, sovereignty must be designed across the stack, from silicon and infrastructure to cloud, data, and AI.
Microsoft and Intel® are working together to help organizations create a more consistent foundation for innovation, while retaining greater choice over where workloads run and how they are controlled. A flexible sovereign architecture allows organizations to place each workload in the right environment, apply the controls it requires, and maintain a more consistent technology and operating foundation as requirements evolve.
Sovereignty must work across the technology stack Sovereignty requirements vary by workload. The right level of control depends on factors such as data sensitivity, regulation, latency, connectivity, operational access, and resilience requirements. This creates a continuum, where organizations can apply different levels of control across public cloud, customer-controlled environments, and trusted partner solutions.
Delivering that flexibility requires the underlying technology layers to work together. The controls that shape where a workload runs, how data is protected, who can access it, and how the environment is verified extend across silicon, infrastructure, cloud, security, data, and AI. This is where the Microsoft and Intel partnership comes into focus.
Together, we are working to create a more consistent foundation across these layers, giving organizations greater choice in where workloads run and how they are controlled. This principle is central to the work between Microsoft and Intel. Azure Local , running on Intel® Xeon® 6 processors, brings distributed Azure infrastructure together with a common compute foundation for customer-controlled environments.
Organizations can extend cloud and AI capabilities closer to their data and operations while maintaining consistent approaches to security, management, and governance. Trust must be designed for and verified For sensitive workloads, trust must be supported by technical protections and verifiable evidence. Organizations need to protect workloads across the technology stack and verify that systems are operating under the security conditions they have established.
That starts at the compute layer and extends through identity, encryption, governance, policy, and operations. For supported Azure Confidential VM configurations, Intel® Xeon® processors provide hardware-based protections, including Intel® Trust Domain Extensions (Intel TDX) , which can help isolate workloads while they are in use. Hardware-backed attestation can provide evidence that helps organizations verify relevant security properties of the environment against their requirements.
Microsoft extends this foundation through security and governance controls across cloud and customer-controlled environments. Together, these capabilities can help organizations verify relevant security properties of the environment against their requirements. This is particularly important as AI brings regulated, proprietary, and mission-critical data into new workloads.
Each workload should receive protections proportionate to its risk. Isolation, encryption, attestation, and operational controls can be applied where they provide meaningful value while preserving a manageable operating model across the broader environment. Digital sovereignty requires trust across the technology stack.
Together, Intel and Microsoft are combining hardware-based security and confidential computing with cloud, AI and distributed infrastructure to help customers protect sensitive workloads and verify the environments in which they run. This shared foundation gives organizations greater control, confidence and choice, with the performance and scale to continue innovating as AI moves into more regulated and mission-critical environments.
— Anand Pashupathy, Vice President and General Manager, Intel Product Assurance and Security Sovereign AI brings intelligence closer to data Much of the data with the greatest potential for AI must remain within defined operational, regulatory, or security boundaries. This includes data tied to industrial operations, government services, clinical environments, intellectual property, and proprietary research.
Workload placement has therefore become a practical AI decision. Organizations need the flexibility to run AI closer to their data, applications, and operations. Delivering that flexibility requires infrastructure, cloud platforms, AI software, and security capabilities to work as a connected foundation.
Microsoft Foundry helps organizations build and deploy AI applications and models, with options for where AI runs based on their requirements.
Originally published at azure.microsoft.com
