Comparative Assessment of Artificial Intelligence-Supported Security Measures and Conventional Security Approaches on Banditry and Insurgency activities in Nigeria
DOI:
https://doi.org/10.64348/zije.2026322Abstract
This study examined the role of Artificial Intelligence (AI) in enhancing security operations and its comparative effectiveness relative to conventional security approaches in curbing banditry and insurgency in Nigeria. The study adopted a quantitative research design, utilizing a structured questionnaire administered to a sample of 250 respondents drawn from security agencies and related stakeholders. Data were analysed using Chi-square and multiple regression techniques to test the stated hypotheses. Findings from the Chi-square analysis revealed a statistically significant relationship between AI adoption and reduced response time to banditry and insurgency incidents (χ² = 158.5, p < 0.05), indicating strong respondent agreement that AI enhances operational responsiveness. Further regression results showed that AI-supported security measures had a stronger positive influence on operational effectiveness (β = 0.65, p < 0.001) compared to conventional security approaches (β = 0.28, p < 0.001). The model explained 72% of the variance in security effectiveness (R² = 0.72), demonstrating substantial predictive power. The findings suggest that AI-driven tools as predictive analytics, automated surveillance, and real-time intelligence processing significantly strengthen proactive security management. The study concluded that integrating AI into Nigeria’s security framework enhanced threat detection, improve response efficiency, and complement conventional methods in addressing persistent insecurity challenges.
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