Integrating AI Technologies Into Information Security Systems

This article explores the integration of artificial intelligence (AI) technologies into information security systems, aiming to enhance the effectiveness of threat detection and response. The research is grounded in a comprehensive review of existing literature. It examines AI’s capabilities in processing large volumes of data, forecasting potential threats, and automating their identification. The discussion also addresses associated vulnerabilities, including issues related to the quality of training datasets, susceptibility to adversarial attacks, and algorithmic bias. Special attention is given to the development of technological solutions designed to protect AI-driven systems themselves. These include data encryption, access control mechanisms, anomaly detection systems, behavioral analytics, and architectural strategies such as multi-layered defense and containerization. The findings presented are relevant to cybersecurity researchers and practitioners, machine learning specialists, and developers of intelligent systems engaged in building interdisciplinary approaches to threat analysis, prediction, and mitigation in today’s complex digital environments. This material is also of value to professionals seeking to bridge theoretical frameworks with practical implementation in the context of rapidly evolving digital ecosystems and critical infrastructure.