Abstract
As cybercrimes are becoming increasingly complex, it is imperative for cybersecurity measures to become more robust and sophisticated. The crux lies in extracting patterns or insights from cybersecurity data to build data-driven models, thus making the security systems automated and intelligent. To comprehend and analyze cybersecurity data, several Artificial Intelligence (AI) methods such as Machine Learning (ML) techniques, are employed to monitor network environments and actively combat cyber threats. This study explored the various AI techniques and how they are applied in cybersecurity. A comprehensive literature review was conducted, including a bibliometric analysis and systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Using data extracted from two main scholarly databases: Clarivate's Web of Science (WoS) and Scopus, this article examines relevant academic literature to understand the diverse ways in which AI techniques are employed to strengthen cybersecurity measures. These applications range from anomaly detection and threat identification to predictive analytics and automated incident response. A total of 14,509 peer-reviewed research papers were identified of which 9611 were from the Scopus database and 4898 from the WoS database. These research papers were further filtered, and a total of 939 relevant papers were eventually selected and used. The review offers insights into the effectiveness, challenges, and emerging trends in utilizing AI for cybersecurity purposes.