Artificial Intelligence Integration for Enhancing Digital and Physical Security Systems

Abstract

Artificial Intelligence (AI) has become a central component in modern security ecosystems, encompassing digital security, physical surveillance, threat detection, and automated incident response. This study examines the integration of AI-enhanced security technologies and evaluates their effectiveness through mixed-methods analysis. The research aims to assess the impact of AI-driven threat-detection systems, predictive analytics, automated surveillance, and digital forensic tools on overall security performance, response time, and incident-handling accuracy. Quantitative data were collected from 52,400 security incidents recorded between 2019 and 2024, while qualitative insights were obtained through interviews with cybersecurity analysts, system engineers, and field security officers. Machine-learning models—including random forests, LSTM networks, and convolutional neural networks—were implemented to measure threat-classification accuracy and anomaly-detection performance. Results indicate that AI integration improved threat-detection accuracy by 28%, reduced average incident-response time by 34%, and enhanced digital forensic extraction efficiency by 41%. The study concludes that AI has a transformative impact on multi-layered security environments, enabling faster, more accurate, and more scalable threat mitigation. The research contributes to scientific understanding by providing a comprehensive framework for evaluating AI deployment in hybrid security infrastructures and identifying key challenges related to ethical governance, data privacy, and algorithmic transparency.