AI-Driven Security Systems: Enhancing Real-Time Threat Mitigation in the Digital Age

Abstract

The increasing sophistication and frequency of cyber threats have necessitated the adoption of advanced technologies to enhance real-time threat mitigation. Artificial Intelligence (AI) has emerged as a pivotal tool in cybersecurity, leveraging machine learning (ML) and deep learning (DL) techniques to detect, analyze, and respond to threats in real time. This study explores the capabilities of AI-driven security systems, focusing on their role in predictive threat detection, automated response, and adaptive defense mechanisms. Through a combination of literature review, case studies, and technical analyses, the research identifies the strengths and limitations of AI-driven systems, evaluates their performance against conventional security solutions, and provides actionable insights for their implementation. The findings highlight AI's transformative potential in cybersecurity while emphasizing the need for ethical and transparent practices to address associated challenges such as bias, privacy, and accountability.