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

artificial intelligence cybersecurity AI-enabled threats cyber defense structured literature review

Article Details

How to Cite
Sa’adah, S., Yulianah, Y., Vaquitasari, N. P., Septiana, E., Rasidin, R., & Laksniyunita, W. (2026). The Development of Artificial Intelligence Technology and Cyber Security Threats. Advances in Community Services Research, 4(2), 56–67. https://doi.org/10.60079/acsr.v4i2.877

References

  1. 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
  2. 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
  3. Ajala, O. A. (2024). Leveraging AI/ML for anomaly detection, threat prediction, and automated response. https://doi.org/10.20944/preprints202401.0159.v1
  4. 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
  5. Alam, R. G. G., Hidayah, A. K., Gunawan, G., Wijaya, A., & Abdullah, D. (2025). Manajemen Risiko Keamanan Informasi. PT. Sonpedia Publishing Indonesia.
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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.
  18. 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
  19. 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
  20. Krishnappa, T. (2023). A Review on Artificial Intelligence Techniques in Preventing Cyber. International Journal of Engineering Applied Sciences and Technology, 8(01), 185–189.
  21. 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.
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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.