Agentic AI Framework for Multi-Cloud DevSecOps Automation and Software Supply Chain Security
Keywords:
Agentic AI, Multi-Cloud Computing, DevSecOps Automation, Software Supply Chain Security, Artificial Intelligence Agents, Cloud Security, Continuous Integration and Continuous Deployment, Cybersecurity Automation, Infrastructure as Code, Secure Software DevelopmentAbstract
The rapid adoption of multi-cloud environments has transformed software development and deployment practices, creating complex challenges in DevSecOps automation, security governance, and software supply chain protection. Traditional DevSecOps approaches often depend on rule-based automation, fragmented security tools, and human-driven decision-making, which limits scalability and responsiveness against emerging cyber threats. This research proposes an Agentic Artificial Intelligence (AI) framework designed to enhance multi-cloud DevSecOps automation and strengthen software supply chain security through autonomous, intelligent, and adaptive agents. The proposed framework integrates AI-driven agents capable of continuous monitoring, threat detection, policy enforcement, vulnerability management, compliance assessment, and automated remediation across heterogeneous cloud platforms.
The framework combines machine learning, large language models, security orchestration, infrastructure-as-code validation, and supply chain intelligence to create a proactive security ecosystem. Agentic AI enables autonomous reasoning, collaboration among specialized agents, and dynamic decision-making throughout the software development lifecycle. The research methodology adopts a design science research approach involving framework development, architecture modeling, and evaluation through security automation scenarios. The proposed model aims to reduce operational complexity, improve threat response time, and enhance visibility across distributed cloud environments. The study contributes an intelligent DevSecOps paradigm that supports secure, resilient, and automated software delivery pipelines.
References
1. Bass, L., Weber, I., & Zhu, L. (2015). DevOps: A software architect’s perspective. Addison-Wesley Professional.
2. Beck, K., Beedle, M., van Bennekum, A., et al. (2001). Manifesto for Agile Software Development. Agile Alliance.
3. Meesala, A. (2024). Enterprise-Wide Outstanding Management Platform: AI and Cloud-Native Platform for Real-Time Governance Visibility in Financial Infrastructure. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 357-363.
4. Hossain, M. B., & Rahman, R. (2022). Federated learning: Challenges and future work. World Journal of Advanced Research and Reviews, 15(02), 850-862.
5. Challa, R. (2024, March). Secure hyperconverged infrastructure for government-scale digital transformation: A technical blueprint. International Journal of Research and Applied Innovations, 7(2), 10504–10509.
6. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
7. Kale, P. (2024). AI-Augmented DevSecOps Pipelines for Secure and Efficient Software Delivery in Cloud-Native Platforms. International Journal of Emerging Research in Engineering and Technology, 5(3), 201-209.
8. Kundavaram, R. R., Bandhela, R. R., & Onteddu, A. R. (2025). Quantum support vector regression for high-dimensional data: A hybrid quantum-classical approach (SSRN Scholarly Paper No. 5414059). SSRN. https://doi.org/10.2139/ssrn.5414059
9. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154-164.
10. Gummadi, V. P. K. (2021). Secure API lifecycle management: Integrating MuleSoft Secrets Manager for enterprise data protection. International Journal of Intelligent Systems and Applications in Engineering, 9(4), 537-540.
11. Koganti, H., & Yijie, H. (2018, December). Searching in a Sorted Linked List. In 2018 International Conference on Information Technology (ICIT) (pp. 120-125). IEEE.
12. Rajula, A. (2024). Replication-aware caching for low-latency clinical knowledge retrieval. International Journal of Computer Technology and Electronics Communication, 7(6), 9997–10007.
13. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
14. Prasad, A. (2025). Monitoring and analyzing latency and performance in ultra low latency environments powered by RDMA. International Journal of Applied Mathematics, 38.
15. Macha, Y., & Pulichikkunnu, S. K. (2024). A Data-Driven Framework for Medical Insurance Cost Prediction Using Efficient AI Approaches. IJRAR, 11(4), 887-893.
16. Kanji, R. K. (2020). Federated Learning in Big Data Analytics Privacy and Decentralized Model Training. Journal of Scientific and Engineering Research, 7(3), 343-352.
17. Bhagwat, V. B. (2024). A simplified transition from EBS Payroll to Cloud Payroll: Benefits and Drawbacks. Journal of Computational Analysis and Applications, 33(6).
18. Onik, T. A., Irin, K. N., Azam, M. N., Akter, K. S., Nabil, M. A., Hossain, I., & Akter, S. (2023). Artificial Intelligence-Driven Early Detection of Neurological Disorders and Its Implications for Personalized Rehabilitation Strategies. Vascular and Endovascular Review, 6(2), 104-111.
19. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074-9081.
20. Venkiteela, P. (2024). Strategic API modernization using Apigee X for enterprise transformation. Journal of Information Systems Engineering and Management, 9(4s), 14. https://jisem-journal.com/index.php/journal/article/view/13168
21. Anand, L. (2022). Integrating Kubernetes Microservices with Privileged Access Security and Real-Time Fraud Detection for Modern Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7453-7461.
22. Vollem, S. (2019). Designing a comprehensive observability framework for cloud-native microservices using monitoring platforms to improve system visibility, reliability, and performance analysis. European Journal of Advances in Engineering and Technology, 6(8), 118-129.
23. Pokala, H. K. (2022). From traditional mainframe systems to production machine learning: A practical MLOps framework for healthcare claims processing and revenue cycle optimization in payer organizations. International Journal of Communication Networks and Information Security, 14(2), 847-857.
24. Rahaman, M. M., Biswas, B., & Hossain, M. S. (2022). Dynamic task prioritization in Meta-GNN (graph neural networks) for fraud detection: A meta-reinforcement learning approach with adaptive graph sparsification. International Journal of Science and Engineering Research, 5(1), 207-221.
25. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
26. Wadhwa, R. (2023). Optimizing Enterprise Application Performance Through Event-Driven Microservices and Distributed Database Design. Journal ID, 211, 6317.
27. Konduru, R. K., & Rella, B. P. R. (2025, April). Integrating Renewable Energy Sources for Sustainable Dispatch Solutions for Smart Multi-Load Systems. In 2025 International Conference on Metaverse and Current Trends in Computing (ICMCTC) (pp. 1-9). IEEE.
28. Potdar, A. (2023). Designing secure sovereign cloud architectures for enterprise data analytics and digital transformation. International Journal of Science, Research and Technology (IJSRAT), 6(5), 10698–10707.
29. Awopejo, T. E., Adigun, P. O., & Oyekanmi, T. T. (2023). Emerging applications of artificial intelligence and machine learning in modern earthquake science. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8142–8154.
30. Singh, I. K. (2024). Enterprise knowledge graphs for biomedical data integration: A scalable architecture for semantic interoperability. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8165–8176.
31. Gujarathi, M. (2024). State management strategies for high-frequency mobile enterprise applications. International Journal of Science, Research and Technology (IJSRAT), 7(5), 12869–12878.
32. Chaba, A. (2021). API-driven enterprise commerce architecture for composable digital ecosystems. International Journal of Engineering & Extended Technologies Research, 3(6), 4082–4086.
33. Meesala, L. K. AI-Driven Cyber Defense: A Hybrid Deep Learning Framework for Real-Time Threat Prediction and Response. International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN, 2395-1990.
34. Seetala, S. R. (2025). Architecting autonomous data platforms: Integrating AI-driven governance, metadata intelligence, and data mesh principles. International Journal of Science, Engineering and Technology, 13(1).
35. Vasa, M. R. (2024). Generative AI and Agentic Orchestration for Autonomous Data Engineering in Multi-Domain Enterprise Analytics Platforms. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 364-369.
36. Mudusu, S. K. (2025). Data Engineering Challenges in AI-Driven Healthcare IT Systems: Navigating Real-Time Analytics and Interoperability.
37. Kesarpu, S. (2025). Zero-Trust Architecture in Java Microservices. International Journal of Networks and Security, 5(01), 202-214.
38. Narapareddy, V. S. R., & Yerramilli, S. K. (2022). Scaling the ServiceNow CMDB for distributed infrastructures. International Journal of Engineering Technology Research & Management, 6(10), 101–113.
39. Gollapudi, R. (2022). Risk-controlled near-zero-downtime Oracle database migration using GoldenGate. International Journal of Computational and Experimental Science and Engineering, 8(3), 113–123. https://doi.org/10.22399/ijcesen.5382
