Automating Compliance In Devops Pipelines
DOI:
https://doi.org/10.22399/ijcesen.991Keywords:
DevOps Compliance, Automated Policy Enforcement, Regulatory Integration, AI-Driven Compliance, Secure Software DeliveryAbstract
The expanding popularity of DevOps techniques revolutionized the software delivery pipelines through quick efficient code deployment methods. The research Field of automated compliance detection within DevOps workflows has become essential for solving this problem. This research develops a new conceptual model which ensures regulatory criteria flow naturally throughout every stage of software delivery pipelines. This research approach performs a detailed theoretical evaluation which reveals multiple potential benefits including prompt miscon figuration_errors identification as well as standard policy enforcement throughout cloud settings and better conditions for developers. We identify two forthcoming enhancements for this methodology which comprise artificial intelligence systems for policy development along with multi-cloud network connectivity capabilities. Our research proposal delivers a blueprint for upcoming experimental testing although we prioritize uncovering a unified architecture instead of practical implementation. This research analyzes modern industry conditions while establishing a strategic strategy to place compliance functions directly within DevOps pipelines which results in security risk reduction and accelerated delivery of compliant software solutions. Our methodology helps research communities and practitioners reframe compliance into an integrated dynamic factor within current software development practices to develop more dependable and dependable systems. Organizations achieve regulatory compliance by integrating compliance functions with their DevOps pipeline implementation.
References
Lee, S., Park, H., & Kim, J. (2020). Regulatory compliance challenges in cloud-based DevOps. Future Generation Computer Systems. 111;299-310.
Kurian, T. (2021). High-velocity compliance in regulated industries: A DevOps perspective. Computing and Informatics Journal. 40(6);1497-1512.
Bose, D., & Jana, D. (2019). Ensuring compliance in containerized microservices: Challenges and solutions. International Journal of Software Engineering. 14(3);109-120.
Anders, G., Brown, P., & White, T. (2020). Embracing security automation: A DevOps perspective. IEEE Software, 37(4);22-29.
Cheng, M., & Wu, Y. (2021). The interplay of compliance and DevOps: A systematic literature review. Journal of Systems and Software. 180, 111035.
Marques, L., Oliveira, P., & Tavares, C. (2020). Continuous compliance: A new frontier in DevOps research. Journal of Software: Evolution and Process. 32(9), e2267.
Elias, S., & Ferguson, Z. (2021). Governance as code: Bridging firm-wide policies and DevOps implementations. Information and Software Technology. 133, 106478.
Conway, H., & Shin, D. (2020). Layered policy strategies for continuous compliance in regulated clouds. IEEE Transactions on Cloud Computing. 8(3);511-521.
Miller, A., Tran, T., & Holmes, G. (2023). A Policy-Driven Approach to Microservices Security. IEEE Transactions on Services Computing. 16(2);173–184.
Hashimoto, M., & Wilson, R. (2019). Incremental adoption of automated compliance tools in legacy pipelines. IEEE Cloud Computing. 6(2);46-54.
Zhang, L., & Rosenberg, M. (2019). Toward Automated Compliance Checking in Agile Development. Journal of Software: Evolution and Process, 31(5), e2145.
Castillo, P. & Howard, R. (2021). Policy gating in CI/CD pipelines: A comprehensive overview. Empirical Software Engineering. 26(1);79-93.
Douglas, B., Novak, J., & Regan, P. (2019). Dynamic compliance checks for cloud-native applications. ACM Transactions on Internet Technology. 19(4);1-25.
Park, D., & Lee, J. (2021). Balancing Velocity and Security: Evaluating Cloud-Native DevSecOps Architectures. IEEE Transactions on Dependable and Secure Computing. 18(5);2287–2299.
Feng, Q., Li, D., & Zhou, Z. (2023). End-to-End DevSecOps: Integrating Security and Compliance in CI/CD Pipelines. IEEE Transactions on Cloud Computing. 11(2);310–323.
Garrison, H., & Zhu, M. (2020). Communication frameworks for DevOps compliance. IEEE Transactions on Engineering Management. 67(4);1271-1283.
Flynn, E., & Ahmed, T. (2021). Policy champion roles in enterprise DevOps transformations. Journal of Organizational Computing and Electronic Commerce. 31(2);99-118.
Davis, L., & Chen, A. (2021). Manual overrides in automated compliance checks: Balancing safety and agility. Software Quality Journal. 29(2);225-242.
Bailey, L., & Dorsey, R. (2021). Cross-platform compliance in multi-cloud infrastructures. Journal of Cloud Computing. 9(2);45-53.
Turner, M., West, L., & Karim, N. (2021). Policy Everywhere: A Distributed Approach to DevSecOps Compliance. IEEE Access, 9;95647–95662.
Yu, S., & Alharbi, H. (2021). A Leadership Model for Continuous Security & Compliance in DevOps. Journal of Systems and Software, 172, 110849.
Hafez, I. Y., & El-Mageed, A. A. A. (2025). Enhancing Digital Finance Security: AI-Based Approaches for Credit Card and Cryptocurrency Fraud Detection. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.21
Ibeh, C. V., & Adegbola, A. (2025). AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.19
P. Rathika, S. Yamunadevi, P. Ponni, V. Parthipan, & P. Anju. (2024). Developing an AI-Powered Interactive Virtual Tutor for Enhanced Learning Experiences. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.782
Abu Halka, M., & Nasereddin, S. (2025). The Role of Social Media in Maternal Health: Balancing Awareness, Misinformation, and Commercial Interests. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.1365
J. Prakash, R. Swathiramya, G. Balambigai, R. Menaha, & J.S. Abhirami. (2024). AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.780
V. Saravanan, Tripathi, K., K. N. S. K. Santhosh, Naveenkumar P., P. Vidyasri, & Bharathi Ramesh Kumar. (2025). AI-Driven Cybersecurity: Enhancing Threat Detection and Mitigation with Deep Learning. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1358
Olola, T. M., & Olatunde, T. I. (2025). Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.18
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