Skip to main content
Articles

AI-Driven DevOps for Kubernetes Financial and Healthcare Applications with Real-Time Threat Detection

Tobias Hoßfeld

Independent Researcher, Norway

Abstract

The rapid adoption of cloud-native architectures has transformed the financial and healthcare industries, enabling scalable, resilient, and highly available digital services. At the center of this transformation lies Kubernetes, an orchestration platform that automates deployment, scaling, and management of containerized applications. However, as these industries handle highly sensitive data—ranging from financial transactions to electronic health records—the security, compliance, and operational reliability of Kubernetes environments have become critical concerns. Traditional DevOps approaches, while effective in accelerating software delivery, often struggle to proactively detect and mitigate real-time threats within dynamic container ecosystems.

 

This paper proposes an AI-driven DevOps framework specifically designed for Kubernetes-based financial and healthcare applications, integrating real-time threat detection, predictive analytics, automated compliance validation, and adaptive incident response. By embedding artificial intelligence and machine learning models into the CI/CD pipeline and runtime monitoring layers, the framework enhances observability, anomaly detection, and risk prediction capabilities. The approach leverages behavioral analytics, unsupervised learning for anomaly detection, and reinforcement learning for automated response orchestration.

 

The study highlights how AI-enhanced DevSecOps practices can identify abnormal network traffic, container drift, misconfigurations, privilege escalations, insider threats, and zero-day exploits in real time. Additionally, it addresses regulatory compliance requirements such as HIPAA, PCI-DSS, and GDPR through automated policy enforcement and audit trail intelligence. The methodology integrates telemetry collection, feature engineering, model training, Kubernetes admission controllers, and runtime security engines to create a closed-loop security ecosystem.

 

Through architectural modeling, system simulations, and evaluation of performance metrics such as mean time to detect (MTTD), mean time to respond (MTTR), false positive rates, and system throughput, the research demonstrates significant improvements over traditional monitoring systems. The proposed AI-driven DevOps framework not only strengthens cybersecurity posture but also enhances system reliability, scalability, and operational efficiency.

 

Ultimately, this research contributes to the intersection of AI, DevOps, and Kubernetes security by offering a comprehensive, intelligent framework capable of safeguarding mission-critical financial and healthcare applications in increasingly complex cloud-native environments.

Article Information

Journal

International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

Volume (Issue)

Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

DOI

https://doi.org/10.15662/IJAESIT.2025.0805004

Pages

17212-17221

Published

October 14, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Tobias Hoßfeld (2025). AI-Driven DevOps for Kubernetes Financial and Healthcare Applications with Real-Time Threat Detection. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17212-17221. https://doi.org/10.15662/IJAESIT.2025.0805004

References

1. Devi, C., Vunnam, N., & Jeyaraman, J. (2022). HyperLogLog-based compliance coverage estimation for distributed datasets. Essex Journal of AI Ethics and Responsible Innovation, 2, 495–530.
2. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
3. Paul, D., Sudharsanam, S. R., & Surampudi, Y. (2021). Implementing continuous integration and continuous deployment pipelines in hybrid cloud environments: Challenges and solutions. Journal of Science & Technology, 2(1), 275–318.
4. Mangukiya, M. (2025). Advanced testing and validation frameworks for high-reliability multi-board electronic systems. International Journal of Computational and Experimental Science and Engineering, 11(4).
5. Bairi, A. R., Thangavelu, K., & Keezhadath, A. A. (2024). Quantum computing in test automation: Optimizing parallel execution with quantum annealing in D-Wave systems. Journal of Artificial Intelligence General Science (JAIGS), 5(1), 536–545.
6. Genne, S. (2023). Optimizing user experience in high-traffic financial web applications using analytics. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 7231–7241.
7. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
8. Gaddapuri, N. S. (2022). APPLICATION OF QUANTUM COMPUTING IN DIGITAL EDUCATION SYSTEMS. Power System Protection and Control, 50(2), 12-24.
9. Mudunuri, P. R. (2022). Automating compliance in biomedical DevOps: A policy-as-code approach. International Journal of Research and Applied Innovations (IJRAI), 5(2), 6770–6783.
10. Sriramoju, S. (2025). Architecting scalable API-led integrations between CRM and ERP platforms in financial enterprises. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(4), 10303–10311.
11. Ahuja, D. (2025, August). Intelligent Failure Prediction in CI/CD Pipelines Using Efficient Machine Learning Techniques. In 2025 5th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-7). IEEE.
12. Rao, N. S., Shanmugapriya, G., Vinod, S., & Mallick, S. P. (2023, March). Detecting human behavior from a silhouette using convolutional neural networks. In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) (pp. 943–948). IEEE.
13. Ponnoju, S. C., & Venkatachalam, D. (2024). Containerization efficiency in financial services: Performance enhancement using Kubernetes (EKS) and CI/CD pipelines with Starling. Essex Journal of AI Ethics and Responsible Innovation, 4, 129–168.
14. Itoo, S., Khan, A. A., Ahmad, M., & Idrisi, M. J. (2023). A secure and privacy-preserving lightweight authentication and key exchange algorithm for smart agriculture monitoring system. IEEE Access, 11, 56875–56890.
15. Raju, S., & Sindhuja, D. (2024). Transparent encryption for external storage media with mobile-compatible key management by Crypto Ciphershield. PatternIQ Mining, 1(3), 12–24.
16. Kalabhavi, V. (2025). Integrating Trade Promotion Management With SAP CRM For Enhanced Brand Spend Optimization: A Case Study In The Consumer-Packaged Goods Industry. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(09), 17-22.
17. Gurajapu, A., & Garimella, V. (2025). Secure service-mesh implementations: Mitigating lateral-movement risks in container-based telecom apps. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(1), 11812–11816.
18. Kondisetty, K., Mohammed, A. S., & Muthusamy, P. (2024). Omni-channel customer onboarding with NLP-powered document intelligence. Journal of Artificial Intelligence & Machine Learning Studies, 8, 124–157.
19. Inbavalli, M., & Arasu, T. (2015). Efficient analysis of frequent item set association rule mining methods. International Journal of Scientific & Engineering Research, 6(4).
20. Vishwarup, S., et al. (2020). Automatic person count indication system using IoT in a hotel infrastructure. In 2020 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–4). IEEE.
21. Ramidi, M. (2023). Implementing privacy-focused data sharing frameworks for mobile healthcare communication. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8746–8757.
22. Adepu, R. (2025). Green cloud infrastructure: Energy-aware scheduling and sustainable data center design. International Journal of Computer Technology and Electronics Communication, 8(4), 210–226.
23. Sarabu, V. B. (2018). A framework-driven approach to data validation and reconciliation for operational accuracy. International Journal of Research and Applied Innovations, 1(1), 2130-2140.
24. Kotla, M. R. T. (2023). AI in consumer digital banking: Enabling smart personalization and fraud detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 262–276.
25. Nerella, A., Badri, P., Kandula, S. T. R., Surasani, V. R., Muthukamatchi, P. K., & Jain, A. (2025, August). Neurosymbolic AI for IoT Security: A Knowledge-Guided Framework for Real-Time IoT Anomaly Detection and Response. In 2025 Seventeenth International Conference on Contemporary Computing (IC3) (pp. 1-5). IEEE.
26. Gajula, S. (2023). A Review of Anomaly Identification in Finance Frauds using Machine Learning System. International Journal of Current Engineering and Technology, 13(06).
27. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
28. Shewale, V. (2024). Ransomware Resilience for Pipeline Operators. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7863-7868.
29. Parasa, M. (2024). Intelligent compliance automation in SAP SuccessFactors: AI monitoring for global labor law adherence. International Research Journal of Engineering & Applied Sciences, 12(3). https://doi.org/10.55083/irjeas.2024.v12i03006
30. Namdeo, A. (2024). Autonomous data quality management via ML in cloud warehouses. International Journal of Humanities and Information Technology, 6(04), 124-131.
31. Panyala, V. R. (2022). Integrating AI-driven autoscaling mechanisms in Kubernetes-based microservices architectures. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(4), 9–21.
32. Adepu, G. (2022). Graph AI–Driven Environmental Intelligence Platforms for Predictive Regulatory Risk Assessment. International Journal of Computer Technology and Electronics Communication, 5(5), 5776-5780.
33. Narayanan, S. (2024). Third-party AI vendor risk: Developing assessment frameworks for machine learning service providers. International Journal of Computer Science and Engineering and Information Technology, 10(4), 1133–1142. https://philarchive.org/archive/NARTAV
34. Kunadi, S. K. (2024). From raw data to revenue intelligence: Architecting GTM data platforms for business impact. International Journal of Future Innovative Science and Technology (IJFIST), 7(2), 12414.
35. Akhtaruzzaman, K., MdAbulKalam, A., Mohammad Kabir, H., & KM, Z. (2024). Driving US Business Growth with AI-Driven Intelligent Automation: Building Decision-Making Infrastructure to Improve Productivity and Reduce Inefficiencies. American Journal of Engineering, Mechanics and Architecture, 2(11), 171-198. http://eprints.umsida.ac.id/16412/1/171-198%2BDriving%2BU.S.%2BBusiness%2BGrowth%2Bwith%2BAI-Driven%2BIntelligent%2BAutomation.pdf
36. Ananth, S., Kalpana, A. M., & Vijayarajeswari, R. (2020). A dynamic technique to enhance quality of service in software-defined network-based wireless sensor network (DTEQT) using machine learning. International Journal of Wavelets, Multiresolution and Information Processing, 18(01), 1941020.
37. Kamadi, S. (n.d.). Zero trust architecture implementation in hybrid financial technology ecosystems: A comprehensive framework for regulated environments. Retrieved from ResearchGate.
38. Mulla, F. A. (2024). The mobile revolution during COVID-19: A technical analysis of application evolution. International Journal for Multidisciplinary Research (IJFMR), 6(6), Article 33494.
39. Ponugoti, M. (2024). Engineering global resilience: A cloud-native approach to enterprise system. International Journal of Future Innovative Science and Technology (IJFIST), 7(2), 12392–12403.
40. Prasanna, D., & Manishvarma, R. (2025, February). Skin cancer detection using image classification in deep learning. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–8). IEEE.