An Original Investigation into the Theoretical Basis for AI-Enabled Cybersecurity Operations in Developing Economy Contexts

Artificial intelligence enables significant automation of cybersecurity operations, but its deployment in developing economy contexts faces specific constraints of cost, connectivity, technical capacity, and threat landscape characteristics that are inadequately theorised in literature developed for high-income country contexts. This study developed an original theoretical framework for AI-enabled cybersecurity operations in developing economy contexts, with empirical grounding in Nigeria. A theory-building methodology was employed: systematic review of AI cybersecurity operations literature (81 publications from 2019 to 2024), empirical assessment of AI cybersecurity tool adoption at 20 Nigerian organisations across banking, government, telecoms, and energy sectors through CISO interviews and technology audits, and expert validation. The empirical assessment confirmed that AI security tools were deployed by only 25.0 percent of assessed organisations, with cost (82.0 percent of non-adopters), connectivity requirements (68.0 percent), and talent scarcity for AI tool management (74.0 percent) as dominant barriers. Organisations deploying AI security tools showed significantly lower incident rates (p < 0.001) but also higher false positive rates than international benchmarks, suggesting inadequate local training data. The original AI-Enabled Security Operations Framework for Developing Economies proposes a context-aware AI adoption pathway: lightweight on-premise AI for baseline detection, hybrid cloud AI for advanced analytics, and full AI-integrated SOC as the aspirational endpoint. Original theoretical contributions include the Local Threat Calibration Principle requiring AI model retraining on local threat data. Expert review confirmed the framework's original contribution to AI security deployment theory. Keywords: AI cybersecurity, security operations, developing economies, Nigeria, AI deployment framework