Artificial Intelligence and Machine Learning in Adaptive Cyber Defense: A Threat Intelligence-Driven Framework

Traditional cybersecurity methods are inadequate against evolving cyber threats, necessitating a shift to AI-powered cyber threat intelligence systems for proactive defense. In order to tackle advanced persistent threats (APTs), this study investigates how machine learning (ML) fits into adaptive cyber defence tactics. The use of ML algorithms improves threat detection skills, allowing firms to see trends and abnormalities that might be signs of advanced persistent threats (APTs). Timely reactions to developing risks are made possible by the adaptive nature of machine learning, which allows for continual learning from fresh data. Behavioural analysis, threat intelligence, and intrusion detection systems are some of the areas that are covered in this research. ML approaches that are examined include supervised and unsupervised learning. We also talk about how ML may be integrated with current cybersecurity frameworks to lessen the overall effect of assaults and increase incident response times. The paper emphasizes that AI-powered threat intelligence systems are crucial for modern cybersecurity frameworks, offering scalable, flexible, real-time defenses against complex threats.