The rapid expansion of digital technologies, cloud computing, and interconnected systems has significantly increased the complexity and frequency of cyber threats. Modern attacks such as ransomware, zero-day exploits, phishing campaigns, and advanced persistent threats (APTs) are becoming more sophisticated and difficult to detect using traditional security mechanisms. Conventional rule-based security tools are ineffective against evolving attack patterns and generate a high number of false positives 1. This research paper investigates the integration of Artificial Intelligence (AI) and Machine Learning (ML) into cyber security frameworks to enhance threat detection, prediction, and automated response. Through architectural modeling, comparative analysis, and experimental evaluation, this study demonstrates how AI-driven systems significantly improve detection accuracy, reduce false alarms, and accelerate incident response time. The findings indicate that intelligent cyber defense mechanisms enable proactive, adaptive, and scalable protection for modern digital infrastructures.
Bhalsing et al. (Fri,) studied this question.