Augmenting Cyber Defence: The Transformative Role of Artificial Intelligence in Modern Network Security Infrastructure

Abstract

The exponential growth of cyber threats in contemporary digital ecosystems necessitates innovative defensive strategies that transcend traditional security paradigms. This research examines the transformative integration of Artificial Intelligence (AI) technologies within network security infrastructure, analysing their efficacy in threat detection, response automation, and predictive defence mechanisms. Through a comprehensive mixed-methods approach that combines quantitative performance analysis with qualitative expert assessments, this study evaluates AI-powered security systems across 50 enterprise networks over an 18-month implementation period. Results demonstrate that AI-augmented systems achieve 94.7% threat detection accuracy, with a 78% reduction in false positives, compared to conventional signature-based approaches. Machine learning algorithms exhibit superior capabilities in identifying zero-day exploits and advanced persistent threats (APTs), reducing mean time to detect (MTTD) from 197 days to 3.4 days. The research identifies critical success factors, including quality training datasets, continuous model refinement, and human-AI collaboration frameworks. However, challenges, including adversarial AI attacks, algorithmic bias, and computational resource requirements, warrant strategic consideration. This study provides empirical evidence supporting the adoption of AI in cybersecurity, while offering implementation guidelines for organisations seeking to enhance their security posture through intelligent automation.