We investigate the sociotechnical factors influencing the adoption of AI-based tools in cybersecurity operations within a large international financial organization, using a reflexive thematic analysis grounded in a Sociotechnical Systems (STS) framework. Our qualitative case study involved 15 interviews with security analysts, data scientists, and departmental leaders to explore end-user perspectives, organizational culture, and technical constraints shaping AI adoption. Drawing on established models, we analyze barriers such as mistrust in AI systems, ineffective feedback mechanisms, lack of domain knowledge, and job security concerns. The study reveals a disconnect between the availability of AI tools and their actual use, primarily driven by human-centric resistance and structural inefficiencies rather than technical limitations. These findings emphasize the importance of aligning AI development with analysts’ workflows, increasing explainability, and making design processes more collaborative. We propose a targeted suite of interventions – including training, cross-functional mentorship, and enhanced feedback channels – to support the responsible and effective integration of AI. Our research contributes a theory-informed and empirically grounded understanding of AI adoption challenges in cybersecurity, with practical implications for organizations navigating the human-AI interface in corporate environments.