Artificial Intelligence (AI) Cybersecurity: review paper
Keywords:
Artificial Intelligence, Cybersecurity, Deep Learning, Machine LearningAbstract
The rapid advancement of technology has led up to the amalgamation of artificial intelligence into cybersecurity, with a strong focus on its ability to enhance security protocols. AI is distinctive by its capability to mimic human intelligence and plays a big role in performing repetitive tasks and providing responses to potential threats. The AI technologies discussed in previous studies in this paper, such as machine and deep learning, and others, enable systems to identify and predict vulnerabilities and respond to incidents at present. The literature review in this paper includes studies from 2018 to 2025 that investigation the application and use of (AI) in cybersecurity, its challenges, ethical considerations, and benefits. Notably, data privacy challenges, ethical challenges such as algorithmic biases, and the need for explainable AI (XAI) are highlighted. This research paper has reached many of the findings included in previous studies, revealing gaps in research and emphasizing the need to develop proficiency and implement skills in AI applications in the field of cybersecurity. The researcher used systematic literature review methodology, categorizing and dividing studies according to thematic structure, such as the "National Institute of Standards and Technology" (NIST) Cybersecurity Framework. The results demonstrated that AI can significantly assist defense mechanisms in various areas, including intrusion detection and malware identification, and network anomaly detection. In addition, this research paper discussed the challenges of emerging technologies such as blockchain, and clarified the economic, regulatory, and social implications and future direction of AI in the domain of cybersecurity. The main and fundamental challenges identified included the potential for losses, rising costs, and vulnerabilities in AI systems. This study emphasizes the need to explore new AI technologies and integrate them with other technologies to promote and develop cybersecurity mechanisms. This research ultimately emphasizes the transformative possibility of AI in mitigating cyber threats and enhancing and intensifying comprehensive cybersecurity postures.
References
[1] Burhanuddin, L. A. B., Shibghatullah, A. S. B., Ilias, I. S. C., Zainudin, Z. B., & Zamry, N. B. M. (2025). AI-enhanced cybersecurity: A comprehensive review of techniques and challenges. In M. A. Al-Sharafi, M. Al-Emran, M. A. Mahmoud, & I. Arpaci (Eds.), Current and future trends on AI applications (Vol. 1178, pp. 107–125). Springer, Cham. https://doi.org/10.1007/978-3-031-75091-5_7
[2] Laato, S., Farooq, A., Tenhunen, H., Pitkämäki, T., Hakkala, A., & Airola, A. (2020). AI in cybersecurity education: A systematic literature review of studies on cybersecurity MOOCs. In 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT) (pp. 6–10). IEEE. https://doi.org/10.1109/ICALT49669.2020.00009.
[3] Adewusi, A. O., Okoli, U. I., Olorunsogo, T., Adaga, E., Daraojimba, D. O., & Obi, O. C. (2024). Artificial intelligence in cybersecurity: Protecting national infrastructure: A USA review. World Journal of Advanced Research and Reviews, 21(1), 2263–2275. https://doi.org/10.30574/wjarr.2024.21.1.0313.
[4] Geluvaraj, B., Satwik, P. M., & Ashok Kumar, T. A. (2019). The future of cybersecurity: Major role of artificial intelligence, machine learning, and deep learning in cyberspace. In S. Smys, R. Bestak, J. Z. Chen, & I. Kotuliak (Eds.), International Conference on Computer Networks and Communication Technologies (Vol. 15, pp. 661–668). Springer. https://doi.org/10.1007/978-981-10-8681-6_67.
[5] Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., & Marchetti, M. (2018). On the effectiveness of machine and deep learning for cybersecurity. In 2018 10th International Conference on Cyber Conflict (CyCon) (pp. 371–390). IEEE. https://doi.org/10.23919/CYCON.2018.8405026.
[6] Blancaflor, E. B., Eleccion, F. G., Ferry, F. L., Oplado, J. P., Pajarillo, R. E., & Villaluz, A. (2024). Ethical use of AI for cybersecurity and facing digital threats in the Philippines. In 2024 IEEE 7th International Conference on Computer and Communication Engineering Technology (CCET) (pp. 241–245). IEEE. https://doi.org/10.1109/CCET62233.2024.10837790.
[7] Zhang, F., Cui, X., Wang, Z., Chen, S., Liu, Q., & Liu, C. (2020). A systematic study of AI applications in cybersecurity competitions. In 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE) (pp. 138–146). IEEE. https://doi.org/10.1109/BigDataSE50710.2020.00026.
[8] Bokhari, S. A. A., & Myeong, S. (2023). The influence of artificial intelligence on e-Governance and cybersecurity in smart cities: A stakeholder’s perspective. IEEE Access, 11, 69783-69797. https://doi.org/10.1109/ACCESS.2023.3293480.
[9] Morovat, K., & Panda, B. (2020). A survey of artificial intelligence in cybersecurity. In 2020 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 109–115). IEEE. https://doi.org/10.1109/CSCI51800.2020.00026.
[10] Welukar, J. N., & Bajoria, G. P. (2021). Artificial intelligence in cybersecurity - A review. International Journal of Scientific Research in Science and Technology (IJSRST), 8(6), 488–491. https://doi.org/10.32628/IJSRST218675.
[11] Hariharan, S., Velicheti, A., Anagha, A. S., Thomas, C., & Balakrishnan, N. (2021). Explainable artificial intelligence in cybersecurity: A brief review. In 2021 4th International Conference on Security and Privacy (ISEA-ISAP) (pp. 1–12). IEEE. https://doi.org/10.1109/ISEA-ISAP54304.2021.9689765.
[12] Kaur, H., & Tiwari, R. (2021). Endpoint detection and response using machine learning. Journal of Physics: Conference Series, 2062(1), 012013. https://doi.org/10.1088/1742-6596/2062/1/012013.
[13] Capuano, N., Fenza, G., Loia, V., & Stanzione, C. (2022). Explainable artificial intelligence in cybersecurity: A survey. IEEE Access, 10, 93575–93600. https://doi.org/10.1109/ACCESS.2022.3204171.
[14] Zhang, Z., Ning, H., Shi, F., et al. (2022). Artificial intelligence in cybersecurity: Research advances, challenges, and opportunities. Artificial Intelligence Review, 55, 1029–1053. https://doi.org/10.1007/s10462-021-09976-0.
[15] Kauret, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97, 101804. https://doi.org/10.1016/j.inffus.2023.101804.
[16] Ozkan-Okay, M., et al. (2024). A comprehensive survey: Evaluating the efficiency of artificial intelligence and machine learning techniques on cybersecurity solutions. IEEE Access, 12, 12229–12256. https://doi.org/10.1109/ACCESS.2024.3355547.
[17] Yadav, A., Kumar, A., & Singh, V. (2023). Open-source intelligence: A comprehensive review of the current state, applications, and future perspectives in cybersecurity. Artificial Intelligence Review, 56(11), 12407–12438. https://doi.org/10.1007/s10462-023-10454-y.
[18] Michael, K., Abbas, R., & Roussos, G. (2023). AI in cybersecurity: The paradox. IEEE Transactions on Technology and Society, 4(2), 104-109. https://doi.org/10.1109/TTS.2023.3280109
[19] Choithani, T., Chowdhury, A., Patel, S., & others. (2024). A comprehensive study of artificial intelligence and cybersecurity on Bitcoin, cryptocurrency, and banking system. Annals of Data Science, 11, 103–135. https://doi.org/10.1007/s40745-022-00433-5
[20] Jada, I., & Mayayise, T. O. (2024). The impact of artificial intelligence on organisational cybersecurity: An outcome of a systematic literature review. Data and Information Management, 8(2), 100063. https://doi.org/10.1016/j.dim.2023.100063.
[21] Al Siam, A., Alazab, M., Awajan, A., & Faruqui, N. (2024). A comprehensive review of AI’s current impact and future prospects in cybersecurity. IEEE Access, 13, 14029-14050. https://doi.org/10.1109/ACCESS.2025.3528114
[22] Mohammed, Y. B., Badara, M. S., & Dan’azumi, H. (2024). An intelligence-based cybersecurity approach: A review. Journal of Intelligent Communication, 3(2), 32–43. https://doi.org/10.54963/jic.v4i1.232
[23] Ramos, S., & Ellul, J. (2024). Blockchain for artificial intelligence (AI): Enhancing compliance with the EU AI Act through distributed ledger technology. A cybersecurity perspective. International Cybersecurity Law Review, 5, 1–20. https://doi.org/10.1365/s43439-023-00107-9
[24] Saleh, A. M. S. (2024). Blockchain for secure and decentralized artificial intelligence in cybersecurity: A comprehensive review. Blockchain: Research and Applications, 5, 100193. https://doi.org/10.1016/j.bcra.2024.100193.
[25] Salem, A. H., Azzam, S. M., & Emam, O. E. (2024). Advancing cybersecurity: A comprehensive review of AI-driven detection techniques. Journal of Big Data, 11(105). https://doi.org/10.1186/s40537-024-00957-y
[26] Obioha-Val, O. A., et al. (2025). Cyber espionage in the age of artificial intelligence: A comparative study of state-sponsored campaigns. Asian Journal of Research in Computer Science, 18(1), 184–204. https://doi.org/10.9734/ajrcos/2025/v18i1557.
[27] Radanliev, P. (2024). Cyber diplomacy: Defining the opportunities for cybersecurity and risks from artificial intelligence, IoT, blockchains, and quantum computing. Journal of Cyber Security Technology, 9(1), 28–78. https://doi.org/10.1080/23742917.2024.2312671.
[28] Rafy, M. F. (2024). Artificial intelligence in cybersecurity. SSRN. http://dx.doi.org/10.2139/ssrn.4687831
[29] Patel, A., Pandey, P., Ragothaman, H., Molleti, R., & Peddinti, D. R. (2025). Generative AI for automated security operations in cloud computing. 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC), Houston, TX, USA, 1–7. https://doi.org/10.1109/ICAIC63015.2025.10849302.
[30] Sontan, A. D., & Samuel, S. V. (2024). The intersection of artificial intelligence and cybersecurity: Challenges and opportunities. World Journal of Advanced Research and Reviews, 21(2), 1720–1736. https://doi.org/10.30574/wjarr.2024.21.2.0607
[31] Ajayi, A. J., Joseph, S. A., Metibemu, O. C., Olutimehin, A. T., Balogun, A. Y., & Olaniyi, O. O. (2025). The impact of artificial intelligence on cybersecurity in digital currency transactions. Archives of Current Research International, 25(2), 329–351. https://doi.org/10.9734/acri/2025/v25i21090.
[32] Jain, P., Verma, P., Debnath, T., & Balouria, S. (2025). Cybersecurity forensics with AI. In Quantum computing (Chap. 10). https://doi.org/10.1201/9781003499459-10
[33] Sharko, A. D., Sharko, G., & Qose, S. (2024). Artificial intelligence in cybersecurity applications. In Proceedings of the 2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES) (pp. 175-180). IEEE. https://doi.org/10.1109/INES63318.2024.10629129
[34] Malatji, M., & Tolah, A. (2024). Artificial intelligence (AI) cybersecurity dimensions: A comprehensive framework for understanding adversarial and offensive AI. AI & Ethics. https://doi.org/10.1007/s43681-024-00427-4
[35] Nour, S. M., & Said, S. A. (2024). Harnessing the power of AI for effective cybersecurity defense. In Proceedings of the 2024 6th International Conference on Computing and Informatics (ICCI) (pp. 98-102). IEEE. https://doi.org/10.1109/ICCI61671.2024.10485059
[36] Dambe, S., Gochhait, S., & Ray, S. (2023). The role of artificial intelligence in enhancing cybersecurity and internal audit. 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE), 88–93. https://doi.org/10.1109/AECE59614.2023.10428353
[37] Balaji, T. S., Nadanasabai, R., Ravikumar, R. J., Sharanyaa, S., Meenal, R., & Deepthi, N. P. (2024). Research on the application of artificial intelligence in cybersecurity: Integrating advanced technologies to improve threat detection and response. Proceedings of the 2024 5th IEEE Global Conference for Advancement in Technology (GCAT), 1–6. https://doi.org/10.1109/GCAT62922.2024.10923991
[38] Nageab, W. M., Alrasheed, R., & Khalifa, M. (2024). Cybersecurity in the era of artificial intelligence: Risks and solutions. 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 240–245. https://doi.org/10.1109/ICETSIS61505.2024.10459584
[39] Ankalaki, S., Atmakuri, A. R., Pallavi, M., Hukkeri, G. S., Jan, T., & Naik, G. R. (2025). Cyber attack prediction: From traditional machine learning to generative artificial intelligence. IEEE Access, 13, 44662–44706. https://doi.org/10.1109/ACCESS.2025.3547433
[40] Tariq, U., Ahmed, I., Bashir, A. K., & Shaukat, K. (2023). A critical cybersecurity analysis and future research directions for the Internet of Things: A comprehensive review. Sensors, 23(8), 4117. https://doi.org/10.3390/s23084117
