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Red Hat unifies AI and tactical edge deployment for UK MOD

Simon Osuji by Simon Osuji
February 11, 2026
in Artificial Intelligence
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Red Hat unifies AI and tactical edge deployment for UK MOD
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The UK Ministry of Defence (MOD) has selected Red Hat to architect a unified AI and hybrid cloud backbone across its entire estate. Announced today, the agreement is designed to break down data silos and accelerate the deployment of AI models from the data centre to the tactical edge.

For CIOs, it’s part of a broader move away from fragmented and project-specific AI pilots toward a more platform engineering approach. By standardising on Red Hat’s infrastructure, the MOD aims to decouple its AI capabilities from underlying hardware, allowing algorithms to be developed once and deployed anywhere—whether on-premise, in the cloud, or on disconnected field devices.

Red Hat industrialises the AI lifecycle for the MOD

The agreement focuses on the Defence Digital Foundry, the MOD’s central software delivery hub. The Foundry will now provide a consistent MLOps environment to all service branches, including the Royal Navy, British Army, and Royal Air Force.

At the core of this initiative is Red Hat AI, a suite that includes Red Hat OpenShift AI. This platform addresses a familiar bottleneck in enterprise AI: the “inference gap” between data science teams and operational infrastructure.

The new agreement will allow MOD developers to collaborate on a single platform, choosing the most appropriate AI models and hardware accelerators for their specific mission requirements without being locked into a single vendor’s ecosystem.

This standardisation is vital for “enabling AI at scale,” according to Red Hat. By unifying disparate efforts, the MOD intends to reduce the duplication that often plagues large government IT programs. The platform supports optimised inference, ensuring that AI models can run efficiently on restricted hardware footprints often found in military environments.

Mivy James, CTO at the UK MOD, said: “Easing access to Red Hat platforms becomes all the more important for the UK Ministry of Defence in the era of AI, where rapid adoption, replicating good practice, and the ability to scale are critical to strategic advantage.”

Bridging legacy and autonomous systems

A major hurdle for defence modernisation is the coexistence of legacy virtualised workloads with modern, containerised AI applications. The agreement includes Red Hat OpenShift Virtualization, which provides a “well-lit migration path” for existing systems. This allows the MOD to manage traditional virtual machines alongside new neural networks on the same control plane to reduce operational complexity and cost.

The MOD deal also incorporates Red Hat Ansible Automation Platform to drive enterprise-wide AI automation. In an AI context, automation is the enforcement mechanism for governance. It ensures that as models are retrained and redeployed, the underlying configuration management, security orchestration, and service provisioning remain compliant with rigorous defence standards.

Security and ecosystem alignment

Deploying AI in defence naturally requires a “consistent security footprint” that can withstand sophisticated cyber threats.

The Red Hat platform enables DevSecOps practices, integrating security gates directly into the software supply chain. This is particularly relevant for maintaining a trusted software pedigree when integrating code from approved third-party providers, who can now align their deliverables with the MOD’s standardised Red Hat environment.

Joanna Hodgson, Regional Manager for the UK and Ireland at Red Hat, commented: “Red Hat offers flexibility and scalability to deploy any application or any AI model on their choice of hardware – whether on premise, in any cloud, or at the edge – helping the UK Ministry of Defence to harness the latest technologies, including AI.”

The deployment shows that AI maturity is moving beyond the model itself to the infrastructure that supports it. Success in high-stakes environments like defence depends less on individual algorithm performance and more on the ability to reliably deliver, update, and govern those models at scale.

See also: Chinese hyperscalers and industry-specific agentic AI

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