Abstract: Artificial intelligence (AI) is a must-have tool for cross-disciplinary areas in both economic and social domains to prevent cyberattacks and increase the system's security. Artificial intelligence (AI) is a discipline of computer science that focuses on creating machines capable of human-like thoughts and functioning. These intelligent machines solve many cybersecurity issues, such as user authentication and authorization, automated incident response, phishing and fraud detection, adaptive and predictive security, vulnerability management, intrusion detection, and countermeasures. The artificial intelligence mainly depends on the datasets to build an automated intelligent model. The present study discovers the available datasets for different types of cybersecurity threats. In this study, researchers attempted to explore the dimensions of AI techniques, such as Machine Learning, Natural Language Processing, Data Mining, Deep Learning, IoT, and signature-based techniques, GEN AI, LLM, and QLM, to mitigate cyber threats. The research anticipates that this paper will serve as a valuable resource for all communities in artificial intelligence and cybersecurity, including researchers, developers, and security professionals interested in using XAI models to address complex challenges in cybersecurity. As technology involvement and its adoption within organizations grow, the organizations are more prone to cyber threats and attacks. Therefore, the goal of the presented study was to evaluate the overall impact of artificial intelligence (AI)-driven solutions on organizational cybersecurity with a future roadmap. The authors found that for a strong AI-based system to prevent cybersecurity threats, researchers require an industrybased, balanced, and dynamic dataset with proper attributes.
Pamnani et al. (Wed,) studied this question.