The Place of Generative Artificial Intelligence in Digital Security Studies

Year 2025, Volume: 14 Issue: 4, 2712 - 2734, 31.12.2025

Sevinç Ay , Songül Karakuş

https://doi.org/10.17798/bitlisfen.1780997

Abstract

Digital security has become critically important today as cyber threats continue to diversify. This study aims to systematically examine the place of generative artificial intelligence in the digital security literature. In this context, documents obtained from a search using the keywords generative artificial intelligence and cybersecurity or information security have been compiled from the Web of Science (WoS) and Scopus databases as of September 3, 2025. As a result of the compilation, 37 duplicate documents were removed, and the remaining 350 papers were analyzed using RStudio, VOSviewer, and Gephi. The research covers themes such as the distribution of academic studies by year, author productivity, collaboration networks, country, institution, resource allocation, keywords, and topics covered. The findings reveal that research in the field increased particularly between 2024 and 2025. According to Lotka's law, author productivity indicates that most authors contribute with a single publication, while a small number of productive authors have played a central role in the development of the field. The keyword analysis demonstrates that generative AI research is developing in two directions, both in the context of health/data privacy and cybersecurity/threat analysis. Finally, a country-by-country analysis reveals that the USA and India are the leading countries contributing most to the field, while the rate of international collaboration is low. In conclusion, this study demonstrates that generative AI is an important interdisciplinary research theme in digital security and is expected to guide future studies.

Keywords

Generative artificial intelligence , Information security , Cyber security , Digital security , Bibliometric analysis.

Ethical Statement

The study is complied with research and publication ethics.

References

• B. Ersöz and H. İ. Bülbül, “Üretken yapay zekâ fırsatlar ve tehditler: Bibliyometrik analiz,” in Proc. UBMK 2024 – IXth International Conference on Computer Science and Engineering, 2025, pp. 337–342.
• E. Güler, S. Uğur, and C. Güler, “Eğitim fakültesi öğretim elemanlarının üretken yapay zekâya yönelik farkındalıklarının belirlenmesi ve öğretmen yetiştirmede kullanımına yönelik öneriler,” Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, no. 74, pp. 618–647, 2025.
• M. Herrador and J. Rehberger, “SpAIware: Uncovering a novel artificial intelligence attack vector through persistent memory in LLM applications and agents,” Future Generation Computer Systems, early access, vol. 174, Art. no. 107994, 2026.
• I. Aldasoro, S. Doerr, L. Gambacorta, S. Notra, T. Oliviero, and D. Whyte, “Generative artificial intelligence and cyber security in central banking,” Journal of Financial Regulation, vol. 11, no. 1, pp. 119–128, 2025.
• Z. Hatipoglu, B. Yaman, S. Ceylan, and U. Kose, “Cyber security training with generative artificial intelligence supported web platform using IoT cyber threat scenarios,” in Proc. 2024 Cyber Awareness and Research Symposium (CARS), IEEE, Oct. 2024, pp. 1–6.
• S. Ankalaki, A. A. Rajesh, M. Pallavi, G. S. Hukkeri, T. Jan, and G. R. Naik, “Cyber attack prediction: From traditional machine learning to generative artificial intelligence,” IEEE Access, vol. 13, pp. 44662–44706, 2025.
• B. S. Sarikaya and Ş. Bahtiyar, “GenAI-based jamming and spoofing attacks on UAVs,” IEEE Access, vol. 13, pp. 107596–107620, 2025.
• L. Coppolino, S. D’Antonio, G. Mazzeo, and F. Uccello, “The good, the bad, and the algorithm: The impact of generative AI on cybersecurity,” Neurocomputing, vol. 623, Art. no. 129406, 2025.
• M. A. Ferrag, F. Alwahedi, A. Battah, B. Cherif, A. Mechri, N. Tihanyi, T. Bisztray, and M. Debbah, “Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities,” Internet of Things and Cyber-Physical Systems, pp. 1–46, 2025.
• W. Badawy, “6G-enabled IoT networks cyber threat prevention using generative AI,” in Proc. 2024 International Conference on Future Telecommunications and Artificial Intelligence (IC-FTAI), IEEE, Dec. 2024, pp. 1–4.
• T. N. Shree, K. Sundarakantham, J. Dhivya, and B. Gayathri, “Detecting cyber threat using Gen-AI,” in Proc. 2025 International Conference on Computational, Communication and Information Technology (ICCCIT), IEEE, Feb. 2025, pp. 845–851.
• L. Priya and S. Kapilamithran, “Exploiting AES encryption vulnerabilities through padding oracle attacks and generative AI techniques,” in Proc. 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), IEEE, Oct. 2024, pp. 682–689.
• E. Garfield, “Bradford’s law and related statistical patterns,” Essays of an Information Scientist, vol. 4, pp. 476–483, 1980.
• E. Torun and K. Bozkuş, “Sınıf yönetimi araştırmalarının bibliometrik analizi,” Studies in Educational Research and Development, vol. 6, no. 1, pp. 20–51, 2022.
• Y. Tonta and U. Al, “Türkçe makalelerin dergilere dağılımı ve Bradford yasası,” Bilgi Dünyası, vol. 9, no. 1, pp. 41–66, 2008.
• D. H. Hertzel, “Bibliometrics, history of the development of ideas,” in Encyclopedia of Library and Information Science, A. Kent, Ed. New York, NY, USA: Marcel Dekker, 1987, pp. 144–219.
• M. J. Cobo, A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera, “Science mapping software tools: Review, analysis, and cooperative study among tools,” Journal of the American Society for Information Science and Technology, vol. 62, no. 7, pp. 1382–1402, 2011.
• M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis,” Journal of Informetrics, vol. 11, no. 4, pp. 959–975, 2017.
• Gephi, Gephi—Makes Graphs Handy, 2013. [Online]. Available: https://gephi.org. [Accessed: Sep. 3, 2025]