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Autonomous Agentic AI Architectures for Optimizing Security Operations Centers (SOC) KPIS: Methodology, Impact on Detection, Response, and Recovery Cover

Autonomous Agentic AI Architectures for Optimizing Security Operations Centers (SOC) KPIS: Methodology, Impact on Detection, Response, and Recovery

Open Access
|Sep 2025

References

  1. Agyepong, E., Cherdantseva, Y., Reinecke, P. & Burnap, P. (2022). A systematic method for measuring the performance of a cyber security operations centre analyst. Computers & Security, 117, 102959. Available at: https://doi.org/10.1016/j.cose.2022.102959.
  2. Ali, G., Shah, S., & ElAffendi, M. (2025). Enhancing cybersecurity incident response: AI-driven optimization for strengthened advanced persistent threat detection. Results in Engineering, 21, 104078. Available at: https://doi.org/10.1016/j.rineng.2025.104078.
  3. Arrieta, A.B., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115. Available at: https://doi.org/10.1016/j.inffus.2019.12.012.
  4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. Available at: https://doi.org/10.1145/2939672.2939785.
  5. CICIDS2017 Dataset. (n.d.). Canadian Institute for Cybersecurity. Retrieved from: https://www.unb.ca/cic/datasets/ids-2017.html.
  6. CSE-CIC-IDS2018 Dataset. (n.d.). Canadian Institute for Cybersecurity. Retrieved from: https://www.unb.ca/cic/datasets/ids-2018.html.
  7. CTU-13 Botnet Dataset. (n.d.). Retrieved from: https://github.com/imfaisalmalik/CTU13-CSV-Dataset.
  8. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1, 4171-4186. Available at: https://doi.org/10.18653/v1/N19-1423.
  9. Forsberg, J. & Frantti, T. (2023). Technical performance metrics of a security operations center. Computers & Security, 127, 103529. Available at: https://doi.org/10.1016/j.cose.2023.103529.
  10. Ke, G., et al. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146-3154. Available at: https://www.researchgate.net/publication/378480234_LightGBM_A_Highly_Efficient_Gradient_Boosting_Decision_Tree.
  11. Le, T.D., Le-Dinh, T., & Uwizeyemungu, S. (2025). Cybersecurity analytics for the enterprise environment: A systematic literature review. Electronics, 14(11), 2252. Available at: https://doi.org/10.3390/electronics14112252.
  12. Li, X., Shi, W., Zhang, H., Peng, C., Wu, S., & Tong, W. (2025). The Agentic-AI core: An AI-empowered, mission-oriented core network for next-generation mobile telecommunications. Engineering, 21(6), Article 100503. Available at: https://doi.org/10.1016/j.eng.2025.06.027.
  13. Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2017). Network traffic classifier with convolutional and recurrent neural networks for Internet of Things. IEEE Access, 5, 18042-18050. Available at: https://doi.org/10.1109/ACCESS.2017.2747560.
  14. Omar, L., & Ivrissimtzis, I. (2020). Using theoretical ROC curves for analysing machine learning binary classifiers. Pattern Recognition Letters, 133, 51-58. Available at: https://doi.org/10.1016/j.patrec.2019.10.004.
  15. Roumeliotis, K.I., Tselikas, N.D., & Nasiopoulos, D.K. (2025). Optimizing airline review sentiment analysis: A comparative analysis of LLaMA and BERT models through fine-tuning and few-shot learning. Computers, Materials & Continua, 82(2), 2781-2798. Available at: https://doi.org/10.32604/cmc.2025.059567.
  16. Schesmu, T. (2024). AI-powered SOC: Automating incident response with machine learning and SOAR tools. Medium. Retrieved from: https://medium.com/@akramtalibi1902/ai-powered-soc-automating-incident-response-with-machine-learning-and-soar-tools-70ab343e9402.
  17. Sopan, A., Berninger, M., Mulakaluri, M., & Katakam, R. (2018). Building a machine learning model for the SOC, by the input from the SOC, and analyzing it for the SOC. Proceedings of the 15th IEEE Symposium on Visualization for Cyber Security (VizSec), Article 8709231. Available at: https://doi.org/10.1109/VIZSEC.2018.8709231.
  18. Sowmya, T., & Mary Anita, E.A. (2023). A comprehensive review of AI based intrusion detection system. Measurement: Sensors, 26, 100827. Available at: https://doi.org/10.1016/j.measen.2023.100827.
  19. Wazuh Cloud Demo. (n.d.). Wazuh Inc. Retrieved from https://demo.wazuh.com.
DOI: https://doi.org/10.2478/raft-2025-0046 | Journal eISSN: 3100-5071 | Journal ISSN: 3100-5063
Language: English
Page range: 479 - 493
Published on: Sep 18, 2025
Published by: Nicolae Balcescu Land Forces Academy
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Miroslav Stefanov, Kristyan Stefanov, Laxima Niure Kandel, Sean Crouse, Boyan Jekov, published by Nicolae Balcescu Land Forces Academy
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.