AI-Driven Security Frameworks: Enhancing Threat Detection and Response in Modern Systems

Sandeep Keshetti¹ & Dr Sandeep Kumar²

¹University of Missouri-Kansas City

5000 Holmes St, Kansas City, MO 64110, United States

²Department of Computer Science and Engineering

Koneru Lakshmaiah Education Foundation

Vadeshawaram, A.P., India

Abstract– As cyber threats become more sophisticated and massive, traditional security measures fall behind, necessitating the use of AI-based security frameworks for efficient threat detection and response. This paper analyzes the role of artificial intelligence (AI) in strengthening cybersecurity measures, especially in threat detection, prevention, and real-time response mechanisms in modern systems. The use of AI technologies, such as machine learning (ML), deep learning (DL), and reinforcement learning (RL), in security systems has proven extremely promising in identifying known and new cyber threats, often outperforming traditional security mechanisms. However, there are some gaps between existing research and practical applications. One such primary challenge is model interpretability, as many of these systems operate as “black boxes,” whose decision-making processes are not easy to understand. Moreover, AI’s dependency on large datasets poses data privacy concerns, especially in sensitive environments. Another limitation is the scalability of AI models, particularly when deployed across large and complex network infrastructures, where they may fail to learn and adapt to evolving threats in real-time. Although AI can identify anomalies and potential vulnerabilities, autonomous, adaptive threat response mechanisms lag behind in the early stages of development. This paper identifies these research gaps, the potential of AI in addressing them, and presents recommendations for future advancements in AI-based security frameworks for providing more robust, transparent, and scalable solutions to modern cybersecurity challenges.

Keywords– AI-based security frameworks, threat detection, machine learning, deep learning, reinforcement learning, cybersecurity, anomaly detection, real-time response, autonomous security, network security, data privacy, model interpretability, scalable systems, threat intelligence, zero-day attack detection.

References

Binhammad, M., Alqaydi, S., Othman, A. and Abuljadayel, L. H. (2024) The Role of AI in Cyber Security: Safeguarding Digital Identity. Journal of Information Security, 15, 245-278. doi: 4236/jis.2024.152015.
Patel, M., & Singh, A. (2016). “Machine Learning Approaches in Cybersecurity: A Survey.” Journal of Cybersecurity and Privacy, 2(1), 1-15. https://doi.org/10.1016/j.jcp.2016.05.002
Chaudhary, A., Sharma, R., & Gupta, N. (2019). “Deep Learning for Intrusion Detection Systems in Cloud Computing: A Survey.” IEEE Transactions on Cloud Computing, 7(4), 1090-1103. https://doi.org/10.1109/TCC.2019.2934950
Jones, H., & Miller, D. (2020). “Automating Cyber Threat Intelligence with Deep Learning.” Cybersecurity Innovations, 3(2), 210-225. https://doi.org/10.1109/CI.2020.030123
Ghosh, S., & Dey, P. (2018). “AI in Cybersecurity: Potential, Challenges, and the Future.” Artificial Intelligence Review, 51(3), 1-22. https://doi.org/10.1007/s10462-018-9760-8
Bai, F., Li, Y., & Zhang, Z. (2023). “Reinforcement Learning in Intrusion Prevention Systems: A Study.” Journal of Information Security, 12(1), 100-113. https://doi.org/10.1016/j.jis.2023.01.003
Patil, K., & Reddy, S. (2020). “Graph-Based Machine Learning for Threat Detection in Cybersecurity.” Computers & Security, 89, 101675. https://doi.org/10.1016/j.cose.2020.101675
Liu, Q., Zhang, H., & Zhang, M. (2021). “Network Traffic Anomaly Detection Using Deep Learning Models.” Journal of Network and Computer Applications, 172, 102844. https://doi.org/10.1016/j.jnca.2020.102844
Zhao, Y., & Yang, X. (2018). “AI in Endpoint Security: A Review of Modern Approaches.” Computers & Security, 77, 119-132. https://doi.org/10.1016/j.cose.2018.02.003
Aburomman, M., & Rahman, M. (2017). “Malware Detection Using Machine Learning Algorithms.” International Journal of Computer Science and Information Security, 15(9), 220-229. https://doi.org/10.1016/j.ijcss.2017.06.002
Xu, X., Chen, Y., & Li, L. (2020). “AI for Zero-Day Attack Detection: An Investigation of Deep Learning Models.” Journal of Cybersecurity Technology, 4(3), 154-170. https://doi.org/10.1080/23742917.2020.1837110
Wang, L., & Lee, J. (2022). “Natural Language Processing for Phishing Detection: AI’s Role in Social Engineering Attacks.” International Journal of Artificial Intelligence and Cybersecurity, 5(4), 289-302. https://doi.org/10.1016/j.ijaisc.2022.02.007
Zhang, Z., & Zhang, Y. (2019). “Adversarial Machine Learning in Cybersecurity: Techniques and Challenges.” Computational Intelligence and Security, 15(2), 200-214. https://doi.org/10.1016/j.cis.2019.03.005
Huang, Q., & Xu, S. (2023). “Integrating AI with SIEM for Real-Time Cybersecurity Response.” Journal of Cybersecurity and Data Privacy, 6(2), 105-118. https://doi.org/10.1016/j.jcp.2023.04.008
Nguyen, A., & Pham, H. (2021). “AI for Cybersecurity Automation: Current Trends and Future Directions.” Journal of Computer Networks and Communications, 2021, 2450310. https://doi.org/10.1155/2021/2450310
Li, J., & Wang, X. (2021). “AI in Cyber Defense: Autonomous Response Systems for Network Security.” Journal of Applied Cybersecurity, 7(1), 54-66. https://doi.org/10.1016/j.jac.2021.01.002