Policy Framework for Responsible AI Deployment in the National Cybersecurity Strategy

Artificial Intelligence (AI) is increasingly integrated into critical sectors, including national cybersecurity, raising both opportunities and risks. Concerns over the effects of AI technology on society are raised by its extensive use. In addition to the increasingly divisive aspects of the impact of contentious technologies, such as robots and mass surveillance algorithms, researchers have drawn attention to the negative effects of AI-based systems today, arguing that the creation of tangible governance for AI cannot wait any longer. This paper critically analyses the United Kingdom’s policy framework for responsible AI deployment within its cybersecurity strategy. Using a socio-technical governance lens, the study integrates Responsible AI principles – safety, transparency, accountability, fairness, and human oversight – with the cybersecurity lifecycle (identify, protect, detect, respond, recover). A systematic literature review, guided by the PRISMA framework, identified 13 relevant academic and policy documents published between 2015 and 2025. Findings reveal that the UK’s principle-based, regulator-led model offers flexibility but suffers from fragmentation, inconsistent adoption, and a lack of enforceable mechanisms. Voluntary guidance, limited incident taxonomies, and governance ambiguities restrict effective operationalisation of secure AI. Comparative analysis shows that while the EU AI Act and US NIST frameworks enforce binding standards, the UK remains reliant on soft law and procurement leverage. Ethical dimensions – including fairness, transparency, and contestability – are insufficiently embedded, risking public trust. The study concludes that the UK must balance its pro-innovation stance with enforceable standards, procurement-led compliance, and international harmonisation. Lessons for the Global South, particularly Nigeria, underscore the need for stronger institutional coordination, AI-specific security testing, and capacity building in emerging AI governance frameworks.