Technology Type and Incident Resolution Effectiveness Among Private Security Providers in Nairobi's Industrial Area

This article examines the relationship between the type of security technology and the effectiveness of incident resolution among private security providers in Nairobi's Industrial Area. Using a mixed-methods design that combines a structured survey of 217 respondents with eight key informant interviews, the research finds that technology adoption in the sector is bifurcated: conventional instruments (CCTV, alarms, biometric access control) are near-universally adopted, while computationally advanced tools (AI-based surveillance, drones) remain only marginally adopted. Multiple regression analysis reveals that adoption prevalence does not predict operational contribution; surveillance cameras and AI-based surveillance produced the largest effects on incident resolution, despite vastly different adoption rates, while security drones showed a statistically insignificant effect despite low uptake. These findings suggest that the underadoption of AI-enabled surveillance reflects structural barriers (cost, regulatory ambiguity, personnel competence) rather than limited operational value, whereas drones suffer from poor workflow integration. The study argues that closing Nairobi's private security technology gap requires targeted investment in advanced systems rather than further diffusion of already-saturated conventional tools.