AI-Augmented DevSecOps Architecture for Financial Network Security: Real-Time Threat Detection and Multivariate Risk Modeling
DOI:
https://doi.org/10.15662/IJARCST.2022.0502004Keywords:
DevSecOps, financial network security, real-time threat detection, multivariate risk modeling, anomaly detection, graph neural networks, streaming analytics, continuous delivery, SIEM, observability, explainable AI, federated learning, policy as code, incident orchestrationAbstract
Financial institutions operate within highly regulated, high-value environments where network security threats evolve rapidly and traditional security pipelines often struggle to detect and mitigate attacks in real time. To address these challenges, this work proposes an AI-Augmented DevSecOps architecture that integrates continuous integration/continuous deployment (CI/CD), automated security validation, and artificial intelligence–driven analytics into a unified, adaptive defense framework. The architecture embeds real-time threat detection using streaming machine-learning classifiers, anomaly-aware intrusion detection systems, and behavior-based models capable of identifying zero-day attacks and lateral movement across network segments. In parallel, multivariate risk modeling—supported by deep learning, probabilistic graphical models, and ensemble statistical techniques—quantifies exposure by analyzing heterogeneous signals such as transaction patterns, user behavior, network telemetry, compliance indicators, and system vulnerabilities. By incorporating AI throughout the DevSecOps lifecycle, from secure code pipelines to automated incident response, the proposed architecture enables continuous monitoring, dynamic policy enforcement, and self-optimizing security controls. Experimental results and conceptual validation demonstrate that AI-augmented DevSecOps significantly improves threat prediction accuracy, reduces detection latency, and enhances the resilience of financial networks against sophisticated cyberattacks. This approach provides a scalable blueprint for next-generation financial cybersecurity systems that must operate under strict reliability, transparency, and regulatory constraints.
References
1. Sonatype. (2018). 2018 DevSecOps Community Survey Results (White paper). Sonatype.
2. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
3. Girdhar, P., Virmani, D., & Saravana Kumar, S. (2019). A hybrid fuzzy framework for face detection and recognition using behavioral traits. Journal of Statistics and Management Systems, 22(2), 271-287.
4. Sabin Begum, R., & Sugumar, R. (2019). Novel entropy-based approach for cost-effective privacy preservation of intermediate datasets in cloud. Cluster Computing, 22(Suppl 4), 9581-9588.
5. Mani, K., Pichaimani, T., & Siripuram, N. K. (2021). RiskPredict360: Leveraging Explainable AI for Comprehensive Risk Management in Insurance and Investment Banking. Newark Journal of Human-Centric AI and Robotics Interaction, 1, 34-70.
6. M. A. Alim, M. R. Rahman, M. H. Arif, and M. S. Hossen, “Enhancing fraud detection and security in banking and e-commerce with AI-powered identity verification systems,” 2020.
7. Singh, H. (2020). Evaluating AI-enabled fraud detection systems for protecting businesses from financial losses and scams. The Research Journal (TRJ), 6(4).
8. Sorournejad, S., Zojaji, Z., Atani, R. E., & Monadjemi, A. H. (2016). A survey of credit card fraud detection techniques: Data and technique oriented perspective. arXiv preprint arXiv:1611.06439.
9. Cousin, A. (2013). On multivariate extensions of Value-at-Risk. Journal of Banking & Finance, 37(12), 4657–4670.
10. Huang, W., Weng, C., & Zhang, Y. (2013). Multivariate risk models under heavy-tailed risks. Applied Stochastic Models in Business and Industry, 29(4), 333–348.
11. Estimating value-at-risk using a multivariate copula-based volatility model. (2017). Journal of Computational Finance, 21(3), 45–78.
12. Glass-Vanderlan, T. R., Iannacone, M. D., Vincent, M. S., Chen, Q., & Bridges, R. A. (2018). A survey of intrusion detection systems leveraging host data. arXiv preprint arXiv:1805.06070.
13. Liao, H.-J., Richard Lin, C.-H., Lin, Y.-C., & Tung, K.-Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1), 16–24.
14. Shushi, T. (2020). Multivariate risk measures based on conditional expectation. Insurance: Mathematics and Economics, 91, 35–47.
15. Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 1–12. (Note: duplicated for emphasis in domain literature).
16. Brown, D. J., & others. (2013). A comprehensive review of intrusion detection research (selected). Computer Security Journal, 31(2), 101–129.
17. Jaikrishna, G., & Rajendran, S. (2020). Cost-effective privacy preserving of intermediate data using group search optimisation algorithm. International Journal of Business Information Systems, 35(2), 132-151.
18. Sardana, A., Kotapati, V. B. R., & Shanmugam, L. (2020). AI-Guided Modernization Playbooks for Legacy Mission-Critical Payment Platforms. Journal of Artificial Intelligence & Machine Learning Studies, 4, 1-38.
19. Thangavelu, K., Sethuraman, S., & Hasenkhan, F. (2021). AI-Driven Network Security in Financial Markets: Ensuring 100% Uptime for Stock Exchange Transactions. American Journal of Autonomous Systems and Robotics Engineering, 1, 100-130.
20. Arora, Anuj. "The Significance and Role of AI in Improving Cloud Security Posture for Modern Enterprises." International Journal of Current Engineering and Scientific Research (IJCESR), vol. 5, no. 5, 2018, ISSN 2393-8374 (Print), 2394-0697 (Online).
21. Abdallah, A., Zainal, A., & Maarof, M. (2014). Feature engineering strategies for fraud detection in financial transaction streams. IEEE Transactions on Dependable and Secure Computing, 11(4), 289–301.
22. Kapadia, V., Jensen, J., McBride, G., Sundaramoothy, J., Deshmukh, R., Sacheti, P., & Althati, C. (2015). U.S. Patent No. 8,965,820. Washington, DC: U.S. Patent and Trademark Office.
23. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
24. Jeetha Lakshmi, P. S., Saravan Kumar, S., & Suresh, A. (2014). Intelligent Medical Diagnosis System Using Weighted Genetic and New Weighted Fuzzy C-Means Clustering Algorithm. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 213-220). New Delhi: Springer India.
25. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
26. Chen, T., Xu, R., & Zhang, L. (2019). Graph-based fraud detection in financial networks. Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), 1080–1085.
