The integration of AI into cybersecurity is essential for addressing complex and evolving threats. However, much of the existing AI implementation research emphasises either technical or social dimensions, neglecting their socio-technical interdependence. This study addresses this gap by identifying the critical success factors (CSFs) for AI-driven cybersecurity implementation through a socio-technical lens. Using an interpretive case study of a South African state-owned entity, the research draws on thematic analysis of in-depth interviews with technical staff and end-users. Findings reveal that successful implementation depends on the interplay between technical and social elements. Key technical CSFs include data quality, scalability, automation, and efficiency, while social CSFs encompass change acceptance, top management support, user awareness, ethical considerations, human oversight, and usability. Crucially, the study confirms that neither technical nor social factors alone are sufficient and that effective implementation depends on their interdependence. By applying a socio-technical perspective, the research offers a more balanced understanding of AI-driven cybersecurity and presents a framework to support practitioners in implementing socially integrated, technically robust solutions. Future research should further examine how human-AI collaboration can be socio-technically integrated to enhance trust, ensure ethical compliance, and improve the operational reliability of AI-enabled cybersecurity systems within organisational settings.