AI-Driven Threat Intelligence Adoption in Security Operations Centers: Human Skill Erosion or Analyst Augmentation? A Comparative Case Study of Resource-Constrained SOCs in Emerging Markets

The integration of artificial intelligence (AI) into Security Operations Centers (SOCs) is widely promoted as a remedy for the global cybersecurity workforce deficit, yet the dominant literature draws almost exclusively on large, well-resourced enterprises in Western contexts. This paper addresses a significant empirical gap by examining whether AI-driven threat intelligence tools augment analyst capabilities or accelerate professional deskilling within resource-constrained SOCs operating in Sub-Saharan Africa's telecommunications and banking sectors. Using a comparative case study design across three SOC environments varying in AI adoption maturity, the study employs semi-structured interviews with 31 participants, 180 hours of behavioral observation, and pre/post analyst skills assessments. Findings demonstrate that augmentation and deskilling are not technologically determined outcomes but are mediated by governance structures, training investment, analyst seniority, and whether AI systems are embedded within human decision-making frameworks rather than positioned as substitutes for human judgment. Junior analysts in high-AI-maturity environments exhibit a mean 23% decline in manual log analysis proficiency, while senior analysts at the same sites show marginal skill improvement when effective human-in-the-loop governance is maintained. SOC_s with low AI maturity preserve foundational analyst skills but carry structural detection coverage gaps that AI deployment could address. The paper proposes a governance and training model for resource-constrained emerging market contexts and identifies directions for future empirical validation.