Main Article Content
Abstract
Purpose: This study aims to examine the relationship between advances in Artificial Intelligence (AI) and the evolving cybersecurity landscape by identifying emerging AI-enabled threats, exploring AI-based defense mechanisms, and analyzing the challenges of implementing AI-driven cybersecurity systems.
Research Method: A structured literature review using a qualitative descriptive approach was conducted. Relevant studies published between 2021 and 2025 were systematically identified through major academic databases using predefined search keywords. The selected studies were screened using inclusion and exclusion criteria and analyzed through thematic content analysis.
Results and Discussion: The findings indicate that AI simultaneously functions as a catalyst for increasingly sophisticated cyber threats and as an enabler of advanced cybersecurity defenses. Three dominant themes emerged: the evolution of AI-driven threats, the application of AI in cybersecurity defense, and the technical and organizational challenges associated with AI implementation.
Implications: The study highlights the importance of integrating technological innovation, human oversight, and governance frameworks to strengthen cybersecurity resilience.
Originality: This review provides a holistic perspective by simultaneously examining AI-enabled threats, defensive applications, and implementation challenges within cybersecurity ecosystems.
Keywords
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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- Alam, M. A., Sarna, S. A., Rakibuzzaman, M., & Reza, J. (2025). Strengthening Cybersecurity Protocols to Safeguard U.S. Financial Infrastructure Against Emerging Threats. Advances in Economics & Financial Studies, 3(2 SE-Articles), 71–82. https://doi.org/10.60079/aefs.v3i2.506
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- Bakhronkulova, L., Ali, M., Khabirova, Z., Azimjon, A., Zebo, A., & Muslima, A. (2025). Artificial Intelligence in Cybersecurity: Threats, Defenses, and Future Directions. ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference, 2, 23–30. https://doi.org/10.17770/etr2025vol2.8592
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- Hernandez-Ramos, J. L., Karopoulos, G., Chatzoglou, E., Kouliaridis, V., Marmol, E., Gonzalez-Vidal, A., & Kambourakis, G. (2025). Intrusion Detection based on Federated Learning: a systematic review. ACM Computing Surveys, 57(12), 1–65. https://doi.org/10.48550/arXiv.2308.09522
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- Jackson, K. A. (2023). A systematic review of machine learning enabled phishing. ArXiv Preprint ArXiv:2310.06998. https://doi.org/10.48550/arXiv.2310.06998
- Khan, M. I., Arif, A., & Khan, A. R. A. (2024). The most recent advances and uses of AI in cybersecurity. BULLET: Jurnal Multidisiplin Ilmu, 3(4), 566–578.
- Khan, M. I., Arif, A., & Khan, A. R. A. (2025). The Dual Role of Artificial Intelligence in Cybersecurity : Enhancing Defense and Navigating Challenges. International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 13(1), 62–67. https://doi.org/10.55524/ijircst.2025.13.1.9
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- Krishnappa, T. (2023). A Review on Artificial Intelligence Techniques in Preventing Cyber. International Journal of Engineering Applied Sciences and Technology, 8(01), 185–189.
- Kumar, N., Sen, A. C., Hordiichuk, V., Teresa, M., & Jaramillo, E. (2023). AI in Cybersecurity : Threat Detection and Response with Machine Learning. Tuijin Jishu/Journal of Propulsion Technology, 44(3), 38–46.
- Ling, X., Wu, L., Zhang, J., Qu, Z., Deng, W., Chen, X., Qian, Y., Wu, C., Ji, S., Luo, T., Wu, J., & Wu, Y. (2023). Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art. Computers & Security, 128, 103134. https://doi.org/https://doi.org/10.1016/j.cose.2023.103134
- Markevych, M., & Dawson, M. (2023). A review of enhancing intrusion detection systems for cybersecurity using artificial intelligence (AI). International Conference Knowledge-Based Organization, 29(3), 30–37. https://doi.org/10.2478/kbo-2023-0072
- Mendes, C., & Rios, T. N. (2023). Explainable artificial intelligence and cybersecurity: A systematic literature review. ArXiv Preprint ArXiv:2303.01259. https://doi.org/10.48550/arXiv.2303.01259
- Mohamed, N. (2025). Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms. Knowledge and Information Systems, 67(8), 6969–7055. https://doi.org/10.1007/s10115-025-02429-y
- Nebati, E. E., Ayvaz, B., & Kusakci, A. O. (2023). Digital transformation in the defense industry: A maturity model combining SF-AHP and SF-TODIM approaches. Applied Soft Computing, 132, 109896. https://doi.org/https://doi.org/10.1016/j.asoc.2022.109896
- Ofusori, L., Bokaba, T., & Mhlongo, S. (2024). Artificial Intelligence in Cybersecurity: A Comprehensive Review and Future Direction. Applied Artificial Intelligence, 38(1), 2439609. https://doi.org/10.1080/08839514.2024.2439609
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- Schmitt, M., & Flechais, I. (2024). Digital deception: generative artificial intelligence in social engineering and phishing. Artificial Intelligence Review, 57(12), 324. https://doi.org/10.1007/s10462-024-10973-2
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References
Achuthan, K., Ramanathan, S., Srinivas, S., & Raman, R. (2024). Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions. Frontiers in Big Data, 7, 1497535. https://doi.org/10.3389/fdata.2024.1497535
Afolalu, O., & Tsoeu, M. S. (2025). Artificial Intelligence as the Next Frontier in Cyber Defense: Opportunities and Risks. Electronics, 14(24), 4853. https://doi.org/10.3390/electronics14244853
Ajala, O. A. (2024). Leveraging AI/ML for anomaly detection, threat prediction, and automated response. https://doi.org/10.20944/preprints202401.0159.v1
Alam, M. A., Sarna, S. A., Rakibuzzaman, M., & Reza, J. (2025). Strengthening Cybersecurity Protocols to Safeguard U.S. Financial Infrastructure Against Emerging Threats. Advances in Economics & Financial Studies, 3(2 SE-Articles), 71–82. https://doi.org/10.60079/aefs.v3i2.506
Alam, R. G. G., Hidayah, A. K., Gunawan, G., Wijaya, A., & Abdullah, D. (2025). Manajemen Risiko Keamanan Informasi. PT. Sonpedia Publishing Indonesia.
Arif, A., Khan, M. I., & Khan, A. R. A. (2024). An overview of cyber threats generated by AI. International Journal of Multidisciplinary Sciences and Arts, 3(4), 67–76. https://doi.org/10.47709/ijmdsa.v3i4.4753
Bakhronkulova, L., Ali, M., Khabirova, Z., Azimjon, A., Zebo, A., & Muslima, A. (2025). Artificial Intelligence in Cybersecurity: Threats, Defenses, and Future Directions. ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference, 2, 23–30. https://doi.org/10.17770/etr2025vol2.8592
Chakraborty, A., Biswas, A., & Khan, A. K. (2023). Artificial Intelligence for Cybersecurity: Threats, Attacks and Mitigation BT - Artificial Intelligence for Societal Issues (A. Biswas, V. B. Semwal, & D. Singh (eds.); pp. 3–25). Springer International Publishing. https://doi.org/10.1007/978-3-031-12419-8_1
Chung, N. C., Chung, H., Lee, H., Brocki, L., Chung, H., & Dyer, G. (2024). False sense of security in explainable artificial intelligence (XAI). ArXiv Preprint ArXiv:2405.03820. https://doi.org/10.48550/arXiv.2405.03820
Diop, M. M., Ba, M., Sylla, K., & Ouya, S. (2025). Artificial Intelligence in Cybersecurity: Applications, Challenges, and Future Developments. 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), 1–6. https://doi.org/10.1109/IRASET64571.2025.11008137
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2023.102642
Ferrag, M. A., Alwahedi, F., Battah, A., Cherif, B., Mechri, A., Tihanyi, N., Bisztray, T., & Debbah, M. (2025). Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities. Internet of Things and Cyber-Physical Systems, 5, 1–46. https://doi.org/10.48550/arXiv.2405.12750
Guembe, B., Azeta, A., Misra, S., Osamor, V. C., Fernandez-Sanz, L., & Pospelova, V. (2022). The Emerging Threat of Ai-driven Cyber Attacks: A Review. Applied Artificial Intelligence, 36(1), 2037254. https://doi.org/10.1080/08839514.2022.2037254
Hernandez-Ramos, J. L., Karopoulos, G., Chatzoglou, E., Kouliaridis, V., Marmol, E., Gonzalez-Vidal, A., & Kambourakis, G. (2025). Intrusion Detection based on Federated Learning: a systematic review. ACM Computing Surveys, 57(12), 1–65. https://doi.org/10.48550/arXiv.2308.09522
Hu, Y., Kuang, W., Qin, Z., Li, K., Zhang, J., Gao, Y., Li, W., & Li, K. (2021). Artificial Intelligence Security: Threats and Countermeasures. ACM Comput. Surv., 55(1). https://doi.org/10.1145/3487890
Jackson, K. A. (2023). A systematic review of machine learning enabled phishing. ArXiv Preprint ArXiv:2310.06998. https://doi.org/10.48550/arXiv.2310.06998
Khan, M. I., Arif, A., & Khan, A. R. A. (2024). The most recent advances and uses of AI in cybersecurity. BULLET: Jurnal Multidisiplin Ilmu, 3(4), 566–578.
Khan, M. I., Arif, A., & Khan, A. R. A. (2025). The Dual Role of Artificial Intelligence in Cybersecurity : Enhancing Defense and Navigating Challenges. International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 13(1), 62–67. https://doi.org/10.55524/ijircst.2025.13.1.9
Khan, N., Ahmad, K., Al Tamimi, A., Alani, M. M., Bermak, A., & Khalil, I. (2025). Explainable AI-based intrusion detection systems for Industry 5.0 and adversarial XAI: A systematic review. Information, 16(12), 1036. https://doi.org/10.3390/info16121036
Krishnappa, T. (2023). A Review on Artificial Intelligence Techniques in Preventing Cyber. International Journal of Engineering Applied Sciences and Technology, 8(01), 185–189.
Kumar, N., Sen, A. C., Hordiichuk, V., Teresa, M., & Jaramillo, E. (2023). AI in Cybersecurity : Threat Detection and Response with Machine Learning. Tuijin Jishu/Journal of Propulsion Technology, 44(3), 38–46.
Ling, X., Wu, L., Zhang, J., Qu, Z., Deng, W., Chen, X., Qian, Y., Wu, C., Ji, S., Luo, T., Wu, J., & Wu, Y. (2023). Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art. Computers & Security, 128, 103134. https://doi.org/https://doi.org/10.1016/j.cose.2023.103134
Markevych, M., & Dawson, M. (2023). A review of enhancing intrusion detection systems for cybersecurity using artificial intelligence (AI). International Conference Knowledge-Based Organization, 29(3), 30–37. https://doi.org/10.2478/kbo-2023-0072
Mendes, C., & Rios, T. N. (2023). Explainable artificial intelligence and cybersecurity: A systematic literature review. ArXiv Preprint ArXiv:2303.01259. https://doi.org/10.48550/arXiv.2303.01259
Mohamed, N. (2025). Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms. Knowledge and Information Systems, 67(8), 6969–7055. https://doi.org/10.1007/s10115-025-02429-y
Nebati, E. E., Ayvaz, B., & Kusakci, A. O. (2023). Digital transformation in the defense industry: A maturity model combining SF-AHP and SF-TODIM approaches. Applied Soft Computing, 132, 109896. https://doi.org/https://doi.org/10.1016/j.asoc.2022.109896
Ofusori, L., Bokaba, T., & Mhlongo, S. (2024). Artificial Intelligence in Cybersecurity: A Comprehensive Review and Future Direction. Applied Artificial Intelligence, 38(1), 2439609. https://doi.org/10.1080/08839514.2024.2439609
Saeed, W., & Omlin, C. (2023). Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, 263, 110273. https://doi.org/https://doi.org/10.1016/j.knosys.2023.110273
Salem, A. H., Azzam, S. M., Emam, O. E., & Abohany, A. A. (2024). Advancing cybersecurity: a comprehensive review of AI-driven detection techniques. In Journal of Big Data (Vol. 11, Issue 1). Springer International Publishing. https://doi.org/10.1186/s40537-024-00957-y
Schmitt, M., & Flechais, I. (2024). Digital deception: generative artificial intelligence in social engineering and phishing. Artificial Intelligence Review, 57(12), 324. https://doi.org/10.1007/s10462-024-10973-2
Thapaliya, S., & Bokani, A. (2024). Leveraging artificial intelligence for enhanced cybersecurity: Insights and innovations. Sadgamaya, 1(1), 46–52. https://nepjol.info/index.php/sadgamaya/article/view/66888.