AI-Augmented DevSecOps Pipelines for Secure and Efficient Software Delivery in Cloud-Native Platforms
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I3P122Keywords:
Artificial Intelligence, DevSecOps, Cloud-Native Computing, CI/CD Pipeline, Machine Learning, Software Security, Continuous Deployment, Cybersecurity Automation, Vulnerability Management, Container SecurityAbstract
Cloud-native architectures have revolutionized software development, offering businesses the opportunity to achieve scalability, flexibility, and constant innovation. At the same time, distributed systems, microservices, containerized deployments and multi-cloud infrastructures have added a number of security challenges along the entire software development lifecycle (SDLC) process. The major focus of traditional DevOps practices is to speed up software delivery by leveraging automation and Continuous Integration/ Continuous Deployment (CI/CD) pipelines. As cyberattacks grew in number and complexity, however, security has become part of the SDLC and DevSecOps has entered the picture. While DevSecOps provides benefits, it can be a challenge for organizations to successfully integrate security into their deployment processes without slowing down the deployment process, causing the system to operate less efficiently or reducing developer productivity. Artificial Intelligence (AI) has become a game-changing technology that can help solve these problems by boosting automation, threat intelligence, and predictive security analytics. AI-enhanced DevSecOps pipelines integrate machine learning, intelligent orchestration, automated vulnerability scanning, anomaly detection, and predictive risk management to create secure and efficient software delivery pipelines in cloud-native environments. Incorporating AI-powered security tools into the CI/CD lifecycle enables organizations to preemptively detect vulnerabilities, focus resources on remediation, minimize false alarms and track system activity in real-time. In this study, the researchers explore how to design and implement AI-enhanced DevSecOps pipelines for cloud-native 0environments. The framework proposed will combine several capabilities, including AI-based static application security testing (SAST), dynamic application security testing (DAST), container image scanning, Infrastructure-as-Code (IaC) security validation, runtime threat intelligence, and automated compliance monitoring. Besides, the framework uses machine learning algorithms to study software artifacts, deployment patterns and operational telemetry data to detect potential security risks prior to production deployment. It explores the ways in which AI-powered automation can help to minimize vulnerability exposure, speed up deployment timelines, and enhance operational resilience. The results of their experiments show that AI-powered DevSecOps pipelines deliver a markedly higher velocity of accurate vulnerability detection, shorter mean time to remediation (MTTR), fewer deployment failures, and better compliance adherence than traditional DevSecOps approaches. The results show better security incident prevention, deployment efficiency and software quality. The findings highlight the need for embedding intelligent security mechanisms to provide security assurance and delivery agility in cloud-native CI/CD pipelines. The new model provides a flexible and scalable solution for enterprises aiming to achieve fast innovation while maintaining comprehensive cybersecurity in today's cloud-based environment.
References
[1] Myrbakken, H., & Colomo-Palacios, R. (2017). DevSecOps: A multivocal literature review. In Software Process Improvement and Capability Determination (pp. 17–29). Springer. https://doi.org/10.1007/978-3-319-67383-7_2
[2] Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176. https://doi.org/10.1109/COMST.2015.2494502
[3] Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6, 35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950
[4] Conti, M., Dehghantanha, A., Franke, K., & Watson, S. (2018). Internet of Things security and forensics: Challenges and opportunities. Future Generation Computer Systems, 78, 544–546. https://doi.org/10.1016/j.future.2017.07.060
[5] Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31. https://doi.org/10.1016/j.jnca.2015.11.016
[6] Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. Proceedings of the IEEE Symposium on Security and Privacy, 305–316. https://doi.org/10.1109/SP.2010.25
[7] Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
[8] Wiafe, I., Koranteng, F. N., Obeng, E. N., Assyne, N., Wiafe, A., & Gulliver, S. R. (2020). Artificial intelligence for cybersecurity: A systematic mapping of literature. IEEE Access, 8, 146598–146612. https://doi.org/10.1109/ACCESS.2020.3013145
[9] Alauthman, M., Aslam, N., Alashhab, Z., Al-Qerem, A., & Al-Omari, E. (2022). Machine learning for cybersecurity applications. Electronics, 11(3), 395. https://doi.org/10.3390/electronics11030395
[10] Taddeo, M., & Floridi, L. (2018). How AI can be a force for good in cybersecurity. Science, 361(6404), 751–752. https://doi.org/10.1126/science.aat5991
[11] Shu, R., Gu, X., & Enck, W. (2017). A study of security vulnerabilities on Docker Hub. Proceedings of the ACM CODASPY Conference, 269–280. https://doi.org/10.1145/3029806.3029832
[12] Kreutz, D., Ramos, F. M. V., Verissimo, P. E., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2015). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14–76. https://doi.org/10.1109/JPROC.2014.2371999
[13] Rahman, A., & Williams, L. (2016). Security practices in continuous deployment pipelines. Proceedings of the 1st International Workshop on Release Engineering, 4–7. https://doi.org/10.1145/2962695.2962707
[14] Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176. https://doi.org/10.1109/COMST.2015.2494502
[15] Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
[16] Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). Machine learning and deep learning approaches for cybersecurity: A review. IEEE Access, 10, 19572–19585. https://doi.org/10.1109/ACCESS.2022.3151248
[17] Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. M. (2018). A survey on security threats and defensive techniques of machine learning: A data-driven view. IEEE Access, 6, 12103–12117. https://doi.org/10.1109/ACCESS.2018.2805680
[18] Wang, Z. (2018). Deep learning-based intrusion detection with adversaries. IEEE Access, 6, 38367–38384. https://doi.org/10.1109/ACCESS.2018.2854599
[19] Rajapakse, R. N., Zahedi, M., Babar, M. A., & Shen, H. (2022). Challenges and solutions when adopting DevSecOps: A systematic review. ACM Computing Surveys, 55(3), 1–41. https://doi.org/10.1145/3496938
[20] Rajapakse, R. N., Zahedi, M., Babar, M. A., & Shen, H. (2021). Challenges and solutions when adopting DevSecOps: A systematic review. arXiv Preprint. https://arxiv.org/abs/2103.08266
