UK government urged to address AI dependency as access risks grow

The UK government is examining the economic and security risks of losing access to advanced US artificial intelligence (AI) models, amid growing concerns over Britain’s reliance on overseas tech.

The government assessment, as reported first by the FT, follows an intervention by the Trump administration in June that temporarily restricted foreign access to Anthropic’s Fable 5 and Mythos 5 models.

The US government applied export controls to the models, requiring Anthropic to restrict access by foreign nationals both inside and outside the US. Anthropic subsequently suspended access for all users because it said it had no reliable way of verifying nationality in real time. The restrictions were lifted on June 30.

The episode has raised a wider question for government as it looks to accelerate the adoption of AI across public services: what happens when organisations become dependent on models whose availability they do not control?

“The central lesson is that access to an AI model should never be mistaken for control over it,” said Chris Newton-Smith, CEO of IO. “This is particularly important as organisations move quickly to adopt AI, often without fully considering the long-term implications.”

IO’s State of Information Security Report 2025, based on research among more than 3,000 information security professionals in the UK and US, found that 54 percent said their organisations had adopted AI technology too quickly and were now facing challenges in scaling it back or implementing it more responsibly.

Newton-Smith said organisations have often approached AI as though it were conventional software – selecting a supplier, integrating its service and assuming it will remain available on broadly the same terms.

But advanced AI models increasingly sit between national security, export controls, commercial policy and geopolitics, meaning availability can change for reasons outside the customer’s control.

For public sector organisations, Newton-Smith said that makes concentration risk and exit planning increasingly important.

“Before embedding a model in a critical service, departments should understand its dependencies, data flows, alternatives and how the service would continue if access were restricted,” he said.

“This does not mean avoiding frontier AI. It means designing for substitution from the outset, with portable data, flexible architecture, contractual protections, tested fallbacks and human-operated alternatives.”

AI joins the critical technology supply chain

Newton-Smith argues AI providers should increasingly be treated in a similar way to critical cloud suppliers and other strategic technology partners, although AI introduces additional considerations.

Alongside resilience, security, data handling and continuity, departments need to consider providers’ ability to change model behaviour, policies or limitations without any corresponding change to the customer’s application.

Assessments should also consider jurisdiction, intellectual property, transparency and security, as well as model-change notifications and whether suppliers can support investigations or redress.

Departments need to look further down the supply chain too, as an AI provider may itself depend on other models, cloud platforms or data sources.

The level of assurance should depend on how AI is being used. Newton-Smith compares a drafting assistant used for low-risk internal communications with a system influencing eligibility, enforcement or access to public services.

“Supplier assurance should be proportionate to the potential impact and criticality of the use case,” he said.

Building resilience into public sector AI

The challenge becomes more complicated as countries take different approaches to regulating AI and controlling access to advanced models.

Newton-Smith said organisations shouldn’t assume that a single global AI deployment will remain legally and operationally consistent everywhere. Instead, they need visibility of where systems, users and data are located, which suppliers are involved and which models underpin individual services.

For government, the CEO said the starting point should be an accurate record of each material AI system, including its purpose, data, dependencies, affected people and accountable owner.

Higher-impact systems should be subject to stronger evaluation, human oversight and routes for appeal and redress, while monitoring should continue after deployment to cover model updates, performance drift, security incidents and regulatory change.

Critical public services also need tested continuity plans, alternative suppliers or models, data portability, exit support and non-AI fallbacks.

“Above all, AI governance must bring together policy, risk, procurement, data, security, legal and operational teams – not operate as an isolated technology programme,” he said.