If the UK’s Strategic Defence Review (SDR) set out how AI will transform the UK’s warfighting capabilities within NATO, the National Security Strategy (NSS) (published only a week later) considers artificial intelligence and UK national security as a whole-of-society challenge – one that spans research ecosystems, regulatory frameworks, industrial policy, and the architecture of public trust. The NSS advances a three-part strategy to govern the UK’s approach: (1) building national capacity, (2) accelerating adoption in key sectors, and (3) deepening understanding of national security risks. Taken together, these aim to ensure that the UK is not only technologically capable, but strategically sovereign and ethically credible in the AI age.
Building national capacity for frontier technologies
The first part of the strategy is clear in its ambition: to make the UK “the most innovative and competitive technology ecosystem in the world”, with artificial intelligence as a flagship sector. This ambition rests on four main building blocks:
1. Data foundations
Data is the fuel of AI – and the NSS emphasises the strategic importance of access to high-quality, secure, and sovereign data infrastructure. This includes the National Data Strategy, which aims to improve data availability across sectors, while safeguarding privacy and operational security. In national security terms, this means ensuring that defence, intelligence, emergency response and law enforcement systems can access, share and interpret data at pace – without compromising legal or ethical boundaries.
Crucially, the strategy hints at the need for data architectures that are interoperable with allies but insulated from adversarial exploitation. In the context of hybrid warfare and disinformation, data governance becomes as much about defence as it is about innovation.
2. Research and innovation
The NSS foregrounds the UK’s research base as a critical asset in the AI race. Institutions such as the Alan Turing Institute and the newly created Frontier AI Taskforce are positioned as national capabilities in their own right, tasked with keeping the UK at the forefront of AI discovery.
In national security terms, the government is investing in high-assurance AI systems – particularly those that can be safely deployed in critical sectors such as defence, intelligence, and energy infrastructure. This links directly to the SDR’s focus on sovereign capability: frontier research must not only advance performance, but support independence from adversarial supply chains and technological dependencies.
3. Investment and scale
The government commits to continuing “strategic and catalytic” investments in frontier tech, including through the National Security Strategic Investment Fund (NSSIF). This hybrid public-private model is designed to de-risk early-stage innovation while shaping the direction of market outcomes.
Where the SDR talked about “wartime pace,” the NSS reframes it as “national resilience at scale.” This includes a recognition that future crises – whether biological, environmental, cyber or military – will be shaped by the UK’s ability to mobilise AI-enabled technologies under pressure.
4. Skills and talent
Finally, this rests on people. Talent pipelines, particularly in AI safety, computer science, and quantum engineering, are identified as both a national advantage and a vulnerability. The NSS outlines ongoing reform to the visa and immigration system to attract global AI researchers, as well as investment in domestic skills via postgraduate funding and upskilling initiatives.
Here the security dimension is implicit: the UK’s AI workforce must not only be competitive but trusted. The strategy points to the need for security clearance reform and talent retention in sensitive sectors – reflecting a growing awareness that technological leadership can be undermined by insider threats, brain drain, or foreign influence.
Accelerating adoption in key sectors
The second theme is about diffusion. It recognises that innovation without adoption does little to secure national interests. The NSS identifies several priority sectors for AI uptake, each with direct security relevance.
National defence and intelligence
In alignment with the SDR, the NSS commits to embedding AI across the “whole of the defence enterprise” – not only in weapons systems or battlefield robotics, but in logistics, decision-making, cybersecurity and command structures. The UK is moving from pilot programmes to operational deployment, with an emphasis on scalable, tested and explainable AI tools.
What’s notable in the NSS is its focus on interagency coordination: AI use cases are being developed not in isolation, but through partnerships between MOD, GCHQ, the Home Office and the National Cyber Security Centre. This cross-sectoral logic extends to alliance coordination, especially within NATO and the Five Eyes community.
Critical national infrastructure
The strategy highlights the role of AI in monitoring, predicting and protecting critical systems – from energy grids and transport networks to water supplies and digital infrastructure. With AI increasingly essential for managing complex, interdependent systems, the challenge is to ensure that its use improves resilience rather than introducing new vulnerabilities.
AI’s application here is often unseen but strategic: anomaly detection, predictive maintenance, and dynamic resource allocation. However, the ethical and regulatory oversight of such systems is still evolving, especially where public trust and safety are concerned.
Cyber defence and disinformation
AI is now a frontline tool in both cyber operations and information warfare. The NSS discusses the dual-use nature of generative AI and large language models, warning of their potential for producing scalable, persuasive disinformation or deepfake content that undermines democratic discourse and crisis response.
The UK’s response includes investment in content authentication technology, digital provenance, and AI-driven anomaly detection. More broadly, the government is promoting a secure-by-design approach: ensuring that AI systems, from inception, include mechanisms for traceability, red-teaming and adversarial testing.
Public sector transformation
Beyond traditional national security sectors, the strategy also sees AI as critical for transforming government operations – from border management and pandemic response to law enforcement and emergency planning. This is where the distinction between “civil” and “national security” AI begins to blur. For example, algorithmic triage in immigration or health can have security implications if it fails to reflect equitable, lawful and auditable design.
Here, the NSS calls for responsible deployment frameworks, building on the Office for AI’s existing work and the proposed AI Regulation White Paper. AI ethics is not treated as a constraint on capability, but as a foundation for long-term trust and stability.
Understanding and mitigating AI-driven risks
The final part of the strategy is the most abstract but the most consequential. It recognises that AI is not just a tool or sector – it’s a security variable in its own right. Its risks may be global, cascading and non-linear. The NSS outlines several key areas of concern:
Systemic risk and misuse
AI’s potential to compound or accelerate national security crises is now formally acknowledged. Whether through accidental escalation, mass manipulation, or supply chain sabotage, AI is seen as a “risk amplifier” in an already unstable global environment.
The strategy commits to strengthening national foresight and risk sensing capabilities, including horizon scanning, scenario planning, and the use of AI to model systemic interdependencies. It also calls for AI-specific risk registers and cross-sectoral simulation exercises.
Frontier AI and existential threats
One of the most striking features of the NSS is its explicit concern with frontier AI models – advanced systems that may approach or exceed human-level general capabilities. While such systems are still in development, the NSS positions them as plausible sources of strategic instability within the current decade.
The UK’s approach includes the Frontier AI Taskforce, tasked with developing safety protocols and advising on risk thresholds. These efforts are integrated with the UK’s international leadership efforts, such as the AI Safety Summit held at Bletchley Park, and collaborations with the US, EU, and international organisations.
Adversarial AI and arms races
The NSS in considering artificial intelligence and UK national security notes that adversaries are unlikely to adopt the UK’s standards-first approach. The risk of an AI arms race – driven by opaque procurement, unregulated competition, and ‘race-to-the-bottom’ design incentives – is now openly discussed in national strategy documents.
The UK’s counter is to lead by example: embedding safety and accountability into AI infrastructure, but also exporting standards through diplomacy, defence collaboration, and technology partnerships. The NSS makes clear that AI alignment – within and beyond NATO – will be a key battleground for digital sovereignty.
Taken together, the three themes within the National Security Strategy related to AI mark a shift in how the UK sees AI – not as a futuristic capability, but as a present-tense infrastructure of power. Where the Strategic Defence Review emphasised AI’s role in modernising kinetic and digital force structures, the NSS sees AI as a determinant of national security in the broadest sense: from energy resilience and cyber defence to public trust and international stability.
The NSS presents artificial intelligence and UK national security as not just about preparing the UK to use AI, but to shape its global trajectory – defining standards, building coalitions, and hardening systems against abuse. It frames the AI challenge as systemic and long-term, requiring whole-of-government coherence and strategic patience.
For those advising or building AI in this space, the message is that national security is not just about tanks and treaties. It’s about who owns the data, who writes the algorithms, and who defines the ethics at the heart of intelligent systems.