Enterprise DevSecOps Architecture using Automated Security Validation Infrastructure as Code and Continuous Risk Assessment
DOI:
https://doi.org/10.15662/IJARCST.2023.0603011Keywords:
Enterprise DevSecOps, automated security validation, Infrastructure as Code, continuous risk assessment, cybersecurity automation, secure software development, cloud security, continuous integration, continuous delivery, policy as codeAbstract
The increasing complexity of enterprise digital infrastructures has created significant challenges in maintaining security while achieving rapid software delivery and operational agility. Traditional security practices that operate separately from development and operations processes are insufficient for modern cloud-native environments. DevSecOps has emerged as an integrated approach that embeds security throughout the software development lifecycle by combining development, security, and operations practices. This research examines enterprise DevSecOps architecture using automated security validation, Infrastructure as Code (IaC), and continuous risk assessment mechanisms. The study explores how automation-driven security frameworks improve vulnerability detection, compliance management, infrastructure consistency, and organizational resilience. The proposed architectural approach integrates security testing pipelines, policy-as-code models, automated configuration validation, threat intelligence, and continuous monitoring systems to establish proactive risk management capabilities. A comprehensive research methodology based on architectural evaluation, technology analysis, and assessment of modern DevSecOps practices is applied to understand effective implementation strategies. The findings indicate that automated security validation and continuous risk assessment significantly enhance security visibility, reduce remediation time, and support secure software delivery at enterprise scale. Infrastructure as Code further strengthens security governance by enabling repeatable, auditable, and controlled infrastructure deployment. The research concludes that enterprise DevSecOps architectures provide a foundation for achieving secure, scalable, and adaptive software ecosystems capable of addressing evolving cybersecurity challenges
References
1. 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.
2. 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.
3. 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.
4. Chaba, A. (2018). A platform-independent API integration architecture for scalable enterprise commerce solution. International Journal of Research Publication in Engineering, Technology and Management, 1(1), 9–13.
5. Raja, G. V. (2023). AI Driven Secure Intelligent Framework for Fraud Detection Cybersecurity and Cloud Based Enterprise Systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9068-9076.
6. Rella, B. P. (2021). Real-time data processing for machine learning: Streaming architectures, challenges, and use cases. IRE Journals, 5(4), 230–236.
7. Meesala, L. K. (2022). Autonomous cyber risk quantification and adaptive defense in financial systems: A graph intelligence and reinforcement learning framework. World Journal of Advanced Research and Reviews, 16(3), 1489-1496.
8. 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.
9. Mohammed, S. (2021). Hybrid cloud architecture strategy for global infrastructure operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(6), 4078-4081.
10. Vollem, S. (2022). Event-driven architectures for real-time financial risk monitoring: Stream processing and complex event analytics in distributed systems. International Journal of Scientific Research in Science, Engineering and Technology, 9(13), 552-565.
11. 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), 8142–815
12. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.
13. Koganti, H. (2022). Performance optimization of enterprise trading systems through API gateway and circuit breaker architecture. International Journal of Future Innovative Science and Technology, 5(2), 8126–8138.
14. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
15. 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.
16. Juvvadi, R. R. (2018). Robotic process automation (RPA) in accounting: Measuring ROI and workforce displacement. International Journal of Research and Applied Innovations (IJRAI), 1(1), 17–21.
17. Hossain, M. B., Rahman, R., & Hoque, K. (2021). Feature-Driven Supervised Learning for Detecting DDoS Attack. International Journal of Science and Research Archive, 4(01), 393-402.
18. Mathew, A. (2023). Sentinel AI: An Investigation into Robust Threat Mitigation Strategies for Artificial Intelligence. Educational Research (IJMCER), 5(5), 108-111.
19. Vasa, M. R. (2022). A Model-Driven Methodology for Customer 360 Data Modernization: Conceptual-to-Physical Data Modeling for Multi-Use-Case Cloud Analytics. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9612.
20. 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.
21. Gopinathan, V. R. (2025). Revolutionizing Revenue Cycle Management in the US Healthcare System Using AI-Powered Cloud Solutions. International Journal of Computer Technology and Electronics Communication, 8(4), 11106-11118.
22. Potdar, A. (2022). Hybrid sovereign cloud framework for artificial intelligence powered enterprise analytics and secure data integration. International Journal of Future Innovative Science and Technology, 5(5), 9254–9265.
23. 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.
24. 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
25. Macha, Y. (2022). A Review of Cloud-Based CRM Systems in Healthcare: Advances, Tools, Challenges, and Best Practices. Int. J. Curr. Eng. Technol, 12(6), 848-856.
26. Challa, R. (2022). Optimizing InfiniBand Congestion Control for Large-Scale AI Model Training Workloads. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5749-5757.
27. Narapareddy, V. S. R., & Yerramilli, S. K. (2022). Risk-oriented incident management in ServiceNow event management. International Journal of Engineering Technology Research & Management, 6(7), 134–149.
28. 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.
29. 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.


