Another big AI headline landed this week. The UK government has announced over £1 billion in funding to boost AI infrastructure, with investments going into compute power, supercomputers, and digital skills.

But while most coverage is focused on the economics and innovation, there’s a far more practical angle for anyone working in security: once AI becomes part of public infrastructure and day-to-day tools, the risks are no longer theoretical.

So what’s actually happening, and why should you care, even if you’re outside the UK? Let’s get into it.

What’s in the UK AI Funding Package?

• A new exascale supercomputer in Edinburgh, set to be one of the most powerful in the world
• £300 million for new AI compute clusters
• Plans to train 7.5 million people to use AI tools across the workforce by 2030

It’s a clear message: AI is no longer just a research topic. It’s a foundational capability governments want embedded into how they operate, hire, and deliver services.

However, that also means a lot more systems, data, and users sitting on top of very powerful technology, and the security implications are significant.

Why This Matters Everywhere

This isn’t just a UK thing. Most countries are either planning similar investments or closely watching what happens when someone moves first.

And AI infrastructure doesn’t stay local. It connects to cloud platforms, third-party providers, global research partners and international supply chains. As soon as that happens, the risks are everyone’s to manage.

So What’s the Cyber Risk?

When you scale AI, you also scale:

• Exposure: supercomputing clusters become high-value targets
• Complexity: pipelines for training and deploying models have many moving parts, and weak spots
• Dependency: if government or enterprise systems rely on these tools, any compromise is a major incident

There’s also concern about poisoned training data, tampered models, and unsecured APIs in the tools being rolled out to millions of users. Once AI becomes part of standard government or enterprise workflows, any flaws or gaps scale with it.

What Security Leaders Should Be Doing

Understand where AI is showing up - From civil service workflows to legal ops and HR platforms, AI is already being rolled into enterprise tools. You can’t protect what you haven’t mapped.

Secure the full lifecycle - AI isn’t just the final tool or output. Data collection, training, tuning and deployment each introduce risks.

Collaborate early - Security needs to work alongside MLOps, engineering and policy teams before models go live. That’s where you build resilience, not after the fact.

Audit the supply chain - If your AI stack includes open-source models or third-party APIs, treat them like any other software dependency. Review, test, and monitor regularly.

Final Word

AI is no longer just a research trend; it’s being built into national infrastructure and everyday operations. That means the security conversation has to catch up fast. If you haven’t started thinking about how your organisation defends its AI footprint, it’s time to get ahead of it.