Skip to main content

Cognitive AI-Orchestrated Security Frameworks for Resilient Serverless and Containerized Enterprise Applications

Adeleye Adewuyi

University of Southern Indiana, Evansville, United States

Abstract

The rapid adoption of serverless computing and containerized architectures has transformed enterprise application development by enabling elastic scalability, rapid deployment, event-driven processing, and efficient resource utilization. However, these environments introduce complex security challenges, including dynamic workloads, ephemeral execution contexts, container vulnerabilities, identity misconfigurations, supply-chain attacks, API abuse, privilege escalation, and rapidly changing attack surfaces. Traditional security mechanisms based on static rules and perimeter-oriented controls are increasingly insufficient for protecting highly distributed cloud-native applications. This paper proposes a Cognitive AI-Orchestrated Security Framework that integrates artificial intelligence, machine learning, behavioral analytics, automated threat intelligence, policy-driven security, and autonomous response mechanisms to improve resilience across serverless and containerized enterprise applications. The proposed framework continuously analyzes application telemetry, container behavior, API activity, identity events, infrastructure signals, and security logs to identify anomalous patterns and predict emerging threats. A cognitive orchestration layer correlates detected events, evaluates risk, selects appropriate mitigation strategies, and coordinates automated security responses across cloud resources. The methodology combines anomaly detection, supervised classification, risk scoring, policy enforcement, continuous monitoring, and self-healing mechanisms. The framework is designed to reduce detection and response time while improving security visibility, operational resilience, and adaptive protection. The research demonstrates how cognitive AI can transform cloud-native security from reactive monitoring toward predictive, context-aware, and automated cyber defense.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 11 No. 5 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2026.1105001

Pages

21961-21973

Published

September 4, 2026
Copyright

Open Access

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

How to Cite

Adeleye Adewuyi (%2026). Cognitive AI-Orchestrated Security Frameworks for Resilient Serverless and Containerized Enterprise Applications. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 5 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21961-21973. https://doi.org/10.15662/ijetemr.2026.1105001

References

1. Zhang, H., Wang, J., Zhang, H., & Bu, C. (2024). Security computing resource allocation based on deep reinforcement learning in serverless multi-cloud edge computing. Future Generation Computer Systems, 151, 152–161. https://doi.org/10.1016/j.future.2023.09.016
2. Narra, S. L. (2025). Human-AI Collaboration in Identity Security: When Should AI Decide?. Journal of Computer Science and Technology Studies, 7(7), 191-197.
3. Jayabalan, K. (2026). Reinforcement learning control in smart energy systems. In Intelligent Control in Smart Energy Systems (pp. 71-88). Elsevier.
4. Patel, C. (2024). AI-driven recommendation systems for improving online customer journey. International Journal of Current Engineering and Technology, 14(6), 549–556.
https://doi.org/10.14741/ijcet/v.14.6.18
5. Vani, M., & Dadlani, D. (2026, July). AI-Driven Root Cause Analysis for Kubernetes Clusters. In 2026 IEEE 9th International Conference on Big Data and Artificial Intelligence (BDAI) (pp. 222-227). IEEE.
6. Kondapalli, K. K., Somajohassula, D. K., & Muppalla, L. K. (2022). Adaptive AI-orchestration and zero-trust security with federated threat intelligence for sustainable enterprise cloud architectures. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 12(1), 176-192.
7. Bellundagi, M. (2023). Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
8. Tarakampet, S., Tatavarthi, S., & Puvvula, G. (2026, July). The Role of AI and Machine Learning in Next-Generation Governance, Risk, and Compliance (GRC) Platforms. In 2026 Seventeenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 507-512). IEEE.
9. Nisar, K. (2024). Prompting, retrieval, and fine-tuning: Foundations of enterprise language model adaptation. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9310-9319.
10. Mali, R. K. (2026, July). AI-Driven Cloud-Native Banking Platforms: A Scalable Architecture for Real-Time Financial Services. In 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS) (pp. 749-756). IEEE.
11. Awopejo, T. E., Adigun, P. O., Oyekanmi, T. T., Azeez, N. A. A., Adekanye, M. A., & Obisesan, A. (2025). Machine learning-based prediction of magnetic properties from hysteresis curves: A comparative study of Random Forest, Gradient Boosting, XGBoost, LightGBM and Support Vector Regressions. International Journal of Research Publications in Engineering, Technology and Management, 8(6), 13456–13479.
12. Soundappan, S. J. (2023). AI-Driven Secure Enterprise Analytics and Intelligent Cloud Data Management Frameworks. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(3), 8236-8242.
13. Mahajan, A. S., Yamsani, N., & Uddandarao, D. P. (2025, November). Actuarial Science Driven Personalization: Optimizing Offers Through Compliance. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-6). IEEE.
14. Mohile, A., Kumar, P., Davis, J., & Mohammed, H. S. (2026, May). An Intelligent Hybrid Framework for Network Intrusion Detection Using Deep and Machine Learning. In 2026 2nd International Conference on Computing, Communication and Green Engineering (CCGE) (pp. 1-6). IEEE.
15. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
16. Pattnaik, M., Jayabalan, K., Nalagandla, R., Arumugam, D., Krishna, I. M., & Padmavathi, N. (2025, November). Enhancing Cross-Border Financial Transactions with AI-Driven Blockchain Solutions-A Deep Neural Network Approach. In 2025 International Conference on Emerging Engineering Technologies and Applications (IC-EETA) (pp. 121-127). IEEE.
17. Vemireddy, S. (2026). Retrieval-Augmented Generation Frameworks for Trustworthy Enterprise AI Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(1), 244-251.
18. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
19. Gopinathan, V. R. (2025). Developing Large Language Model Integrated Cloud-Native DevSecOps for Software Delivery and Continuous Security Validation. International Research Journal of Innovative Engineering, 9(5), 17558-17565.
20. Begum, S., Jobiullah, M. I., Fatema, K., Mahmud, M. R., Hoque, M. R., Ali, M. M., & Ferdausi, S. (2025). AttenGene: A deep learning model for gene selection in PDAC classification using autoencoder and attention mechanism for precision oncology. Well Testing Journal, 34(S3), 705-726.
21. Mirani, A. (2026). Designing AI-native financial systems: Architecture patterns for intelligent enterprise platforms. IPHO-Journal of Advance Research in Science and Engineering, 4(4), 21–33.
22. Tyagi, N. (2024). Deep reinforcement learning for algorithmic trading strategies. International Journal of Research and Applied Innovations, 7(2), 10415-10422.
23. Praneeth, P. (2022). Prediction of Cost Overruns in Solar EPC Projects Using Machine Learning Techniques: A Data-Driven Study in India. International Journal of Engineering Science & Humanities, 12(2), 71-85.
24. Anand, L. (2025). Modernizing Enterprise Systems through Generative AI Autonomous Operations and Cloud-Native Engineering. International Journal of Humanities and Information Technology, 7(02), 54-69.
25. Yepuri, V. K., Polamarasetty, V. K., Donthi, S., & Gondi, A. K. R. (2023). Containerization of a polyglot microservice application using Docker and Kubernetes.arXiv preprint arXiv:2305.00600
26. Chundi, V. R. K., Agarwal, V., & Arya, P. (2025, November). Green AI for Sustainable Supply Chains: Challenges in Emerging Economies. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-5). IEEE.
27. Nisar, K. (2024). Prompting, retrieval, and fine-tuning: Foundations of enterprise language model adaptation. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9310-9319.
28. Kumar, R., Upadhyay, H., Pandey, C. P., & Kumar, P. R. (2026, April). Quantum Computing as a Service (QCaaS): Architecture, Orchestration, and Performance Tradeoffs. In 2026 International Conference on Computing Theory and Wireless Communications (ICCTWC) (pp. 1-11). IEEE.
29. Himeluzzaman, M., Alam, A., Gazi, M. S., Abdullah, S. M., Chy, M. S. K., Onik, T. A., ... & Shakil, S. M. (2025). Countering AI-Generated Disinformation: A Novel Detection Model to Safeguard National Security. International Journal of Computer Technology and Electronics Communication, 8(4), 11192-11203.
30. Bandaru, P. K. (2022). Hardware-in-the-loop testing for connected vehicles: Enhancing software reliability through continuous validation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4645–4651.
31. Mathew, A. (2023). Sentinel AI: An Investigation into Robust Threat Mitigation Strategies for Artificial Intelligence. Educational Research (IJMCER), 5(5), 108-111.
32. Anand, L. (2023). Machine Learning Enabled Enterprise Integration through Intelligent API Governance Secure Cloud Infrastructure and Automated Operations. International Journal of Research and Applied Innovations, 6(3), 5972-5979.
33. Raja, G. V. (2023). AI-Driven Cloud-Native Enterprise Systems Leveraging Kubernetes, DevSecOps, and Predictive Analytics. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7925-7929.
34. Seetharaman, K. M. R. (2025, May). Predicting Cryptocurrency Price Movements Using Leveraging Machine Learning Algorithms. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 1497-1502). IEEE.
35. Awopejo, T. E., Adigun, P. O., Oyekanmi, T. T., Azeez, N. A. A., Adekanye, M. A., & Obisesan, A. (2025). Machine learning-based prediction of magnetic properties from hysteresis curves: A comparative study of Random Forest, Gradient Boosting, XGBoost, LightGBM and Support Vector Regressions. International Journal of Research Publications in Engineering, Technology and Management, 8(6), 13456–13479.
36. Narra, R. (2024). A survey on scalable feature engineering techniques for cloud-native machine learning workflows. International Journal of Advanced Research in Science, Communication and Technology, 4(4), 664–677.
37. Chaturvedi, V., Narra, R., & Chintagunta, S. K. (2026). Applied AI engineering for developers: Building intelligent applications at scale. Wissira Press.
https://doi.org/10.63345/WP-978-93-7559-963-0
38. Padmanabham, S. (2024). AI assisted fraud investigation architecture patterns for technology controls in financial institutions. International Journal of Research Publications in Engineering, Technology and Management, 7(3), 10587–10592.
39. Patel, K., Patel, K., & Thakare, S. B. (2025, October). Adaptive multi-sensor photometric and domain-adaptive fusion based industrial decision support system for intelligent manufacturing operations. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1-6). IEEE.
40. Mohan, A. (2025). Recommender Systems in the Insurance Sector: Personalizing Customer Experiences. Journal of Computer Science and Technology Studies, 7(5), 129-133.
41. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.