A Statistical Evaluation of Artificial Intelligence (AI) and Big Data Analytics on National Security Performance Indicators in Nigeria: A Simulation-Based Analysis

Abstract

The rise in the security threats in Nigeria calls for the adoption of advanced technological systems capable of improving detection accuracy, cyber resilience, and operational responsiveness. While Artificial Intelligence (AI) and Big Data Analytics (BDA) are widely promoted as transformative tools for modern security operations, direct empirical evaluation within Nigeria remains constrained by restricted access to sensitive operational data. This study therefore adopts an operationally grounded simulation approach to examine how AI and BDA could influence key national security performance indicators. Using parameters that are informed by documented Nigerian security practices, policy frameworks, and international benchmarks, 180 paired observations were simulated to represent operational performance pre and post-AI integration across five selected indicators: threat detection accuracy, response time, cyber-attack interceptions, false alarm rates, and a composite operational efficiency index. The model outputs suggest that AI-enabled systems are associated with considerable improvements across all indicators under plausible institutional conditions. Although the findings are derived from simulated data, they are empirically anchored in real operational contexts and provide structured perception into how AI adoption could reform security performance in Nigeria. The study contributes a methodological bridge between empirical constraints and policy-relevant analysis, offering a foundation for pilot implementation and also future empirical validation.