Journal of Artificial Intelligence and Robotics

Artificial Intelligence (AI) is revolutionizing the cybersecurity landscape, offering unprecedented capabilities in threat detection, prevention, and response. The integration of AI-driven technologies in cybersecurity enables organizations to manage the growing volume, sophistication, and complexity of cyber threats. AI systems are capable of analyzing vast datasets, detecting patterns, and identifying anomalies in real-time, which significantly enhances the ability to combat both known and unknown threats. This review explores the multifaceted applications of AI in cybersecurity, delving into machine learning (ML) for anomaly detection, natural language processing (NLP) for threat intelligence, and the use of deep learning in malware analysis. Additionally, it examines the role of AI in automating security operations, predicting potential vulnerabilities, and adapting to evolving attack vectors. Recent advancements in AI research, such as federated learning and self-supervised learning, are expanding the potential of AI-driven cybersecurity solutions. Federated learning promotes decentralized data analysis, enhancing security while maintaining privacy, whereas self-supervised learning reduces dependency on extensive labeled datasets, improving efficiency in identifying sophisticated threats. Despite these ethical dilemmas, and the need for substantial computational resources, are critically analyzed in this manuscript.