The role of AI and machine learning in cybersecurity: Advancements in threat detection, anomaly detection and automated response

Bello, Aminat Bolaji and Ogundipe, Akeem Olakunle and George, Awobelem A. and Anifowose, Olabode (2025) The role of AI and machine learning in cybersecurity: Advancements in threat detection, anomaly detection and automated response. International Journal of Science and Research Archive, 14 (2). pp. 1587-1597. ISSN 2582-8185

The increasing complexity and frequency of cyber threats have prompted organizations to seek more sophisticated defense mechanisms. Traditional signature-based methods and manual threat-hunting processes often fall short against evolving malware, zero-day exploits, and social engineering techniques. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as pivotal tools, enabling automated threat detection, real-time anomaly analysis, and proactive incident response. This review synthesizes current research and practices related to AI-driven cybersecurity, examining supervised and unsupervised learning for threat detection, AI-powered anomaly detection, and real-world industrial applications. The discussion also explores ethical considerations such as adversarial AI and bias, concluding with future directions that include quantum-safe cryptography, AI-augmented security operations centers, and the integration of blockchain for enhanced cybersecurity.