Securing Autonomous Intelligent Automation Through Development, Security, and Operations Framework (DevSecOps) and Cloud-Native Serverless Architecture
DOI:
https://doi.org/10.65141/ject.v2i2.n2Keywords:
Autonomous Intelligent Automation, DevSecOps, cloud-native serverless architecture, efficiency, scalabilityAbstract
With rapidly evolving technology, implementing a secure and efficient way to develop an automation process has become a critical aspect for any organization’s success. With the current state of the technology, a traditional development methodology sometimes falls short in addressing the threats and rapidly changing business requirements. This research explored the implementation of Autonomous Intelligent Automation through the development, security, and operations framework and cloud-native serverless architecture. It aimed to improve the efficiency, accuracy, and scalability of an organization by streamlining the business processes that are time-consuming, repetitive, and voluminous, and leveraging and combining cutting-edge technologies and methodologies in software development. This research used one of the processes currently existing in Isabela State University-Main Campus as a pioneer process to implement Autonomous Intelligent Automation and validate its effectiveness in terms of efficiency, accuracy, and scalability. Autonomous Intelligent Automation is a way to eliminate human error in a process. By means of intelligently mimicking what the end user does to accomplish a task, it results in a quality service that is efficient, accurate, and scalable. Implementing DevSecOps is an approach to combine and integrate the Development (Dev), Security (Sec), and Operations (Ops) to impose security in all phases of development cycle.
References
AeoLogic. (2024). Reducing operational costs with intelligent process automation. https://www.aeologic.com/blog/reducing-operational-costs-with-intelligent-process-automation/
Akinnagbe, O. B. (2024). Human–AI collaboration: Enhancing productivity and decision-making. International Journal of Education, Management, and Technology, 2(3), 387-417. https://doi.org/10.58578/IJEMT.v2i3.4209
Allen, B., & Edmundson, C. (2023). SANS 2023 DevSecOps survey. SANS Institute. https://www.sonatype.com/hubfs/Survey_DevSecOps_2023.pdf
Amazon Web Services. (2025). What is Amazon CloudWatch? https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/WhatIsCloudWatch.html
Bell, E., Harley, B., & Bryman, A. (2022). Business research methods (6th ed.). Oxford University Press. https://global.oup.com/ukhe/product/business-research-methods-9780198869443
Benavides, L. M. C., Tamayo Arias, J. A., Arango Serna, M. D., Branch Bedoya, J. W., & Burgos, D. (2020). Digital transformation in higher education institutions: A systematic literature review. Sensors, 20(11), Article 3291. https://doi.org/10.3390/s20113291
Cloud Security Alliance. (2022). Top threats to cloud computing: The pandemic eleven report. https://cloudsecurityalliance.org/artifacts/top-threats-tocloud-computing-pandemic-eleven/
Dutta, A. H., & J, B. (2024). Impact of serverless computing on scalability and cost-effectiveness in cloud-based applications. International Journal of Research Publication and Reviews, 5(3), 1824–1830. https://doi.org/10.55248/gengpi.5.0324.0869
Gartner Peer Community. (2023). DevSecOps: Strategies, organizational benefits, and challenges. https://www.gartner.com/peer-community/oneminuteinsights/omi-devsecops-strategies-organizational-benefits-challenges-xrd
Humphreys, A. R. (2023). How AI is driving modern higher education administration. Van Allen Strategies. https://vanallenstrategies.com/insights/how-ai-is-driving-modern-higher-education-administration/
IBM Security. (2025). Cost of a data breach report 2025. https://www.ibm.com/reports/data-breach
Kayanja, W., Kyambade, M., & Kiggundu, T. (2025). Exploring digital transformation in higher education setting: The shift to fully automated and paperless systems. Information & Communications Technology in Education, 12(1), 1-15. https://doi.org/10.1080/2331186X.2025.2489800
Kitsantas, T., Georgoulas, P., & Chytis, E. (2024). Integrating robotic process automation with artificial intelligence for business process automation: Analysis, applications, and limitations. Journal of System and Management Sciences, 14(7), 217–242. https://DOI:10.33168/JSMS.2024.0712
Microsoft. (2025). What is DevSecOps? Microsoft Security. https://www.microsoft.com/en-us/security/business/security-101/what-is-devsecops
Ng, K. K. H., Chen, C.-H., Lee, C. K. M., Jiao, J. R., & Yang, Z. (2021). A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives. Advanced Engineering Informatics, 47, Article 101246. https://doi.org/10.1016/j.aei.2021.101246
Nielsen, I., Piyatilake, A., Thibbotuwawa, A., De Silva, M. M., Bocewicz, G., & Banaszak, Z. (2023). Benefits realization of robotic process automation (RPA) initiatives in supply chains. IEEE Access, 11, 1–15. https://doi.org/10.1109/ACCESS.2023.3266293
Othman, A. (2025). The foundations and challenges of AI ethics. Asia e University. https://doi.org/10.13140/RG.2.2.31556.13444
Pan, M., Wang, J., & Wang, J. (2023). Application of artificial intelligence in education: Opportunities, challenges, and suggestions. In Proceedings of the International Conference on Information Technology in Medicine and Education (pp. 623–627). IEEE. https://doi.org/10.1109/ITME60234.2023.00130
Qin, H., & Wang, G. (2022). Benefits, challenges and solutions of artificial intelligence applied in education. In Proceedings of the 11th International Conference on Educational and Information Technology (ICEIT) (pp. 62–66). IEEE. https://doi.org/10.1109/iceit54416.2022.9690739
Rao, V. (2024, April 25). AI will supercharge RPA. LinkedIn. https://www.linkedin.com/pulse/ai-supercharge-rpa-vasu-rao-zvlic
Saleh, S. M., Madhavji, N., & Steinbacher, J. (2025). A systematic literature review on continuous integration and deployment (CI/CD) for secure cloud computing. arXiv. https://doi.org/10.48550/arXiv.2506.08055
Shin, H., Ko, J., & Lee, J. (2020). LogCleaner: Noise-aware log preprocessing to enhance operational log analysis. IEEE Transactions on Software Engineering, 46(3), 234–250.
Szeliski, R. (2022). Computer vision: Algorithms and applications. Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-34372-9
Ta, P. B., Tr?n, M.-N., & Kim, Y. (2022). Serverless computing approach for deploying machine learning applications in edge layer. In Proceedings of the International Conference on Information Networking (ICOIN) (pp. 396–401). IEEE. https://doi.org/10.1109/ICOIN53446.2022.9687209
Tan, P. A. (2024). Optical character recognition (OCR) with CNN-LSTM attention seq2seq. Towards AI. https://towardsai.net/p/artificial-intelligence/optical-character-recognition-ocr-with-cnn-lstm-attention-seq2seq
