AI-Driven Threat Intelligence: Enhancing Cyber Defense with Machine Learning

Abstract

The rapid evolution of cyber threats, including advanced persistent threats (APTs), ransomware, and zero-day exploits, necessitates a shift from traditional security measures to more adaptive and proactive defenses. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies in cybersecurity, offering real-time threat detection, automated response mechanisms, and continuous learning capabilities. This paper presents an AI-powered threat intelligence framework that integrates data collection, processing, anomaly detection, and automated response to enhance cybersecurity resilience. AI-driven models leverage behavioral analysis and pattern recognition to identify cyber threats, reducing human workload and improving threat detection accuracy. Moreover, continuous learning techniques, including reinforcement learning and adversarial training, enable AI systems to adapt to evolving attack strategies. The findings underscore the necessity of AI-driven cybersecurity in safeguarding digital assets, minimizing response times, and strengthening organizational security postures.