The role of AI and machine learning in cybersecurity: Advancements in threat detection, anomaly detection and automated response
Aminat Bolaji Bello, Akeem Olakunle Ogundipe, Awobelem A. George, Olabode Anifowose · International Journal of Science and Research Archive · 2025
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.