The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
The Convergence of AI/ML and DevSecOps: Revolutionizing Software Development
Open accessAbstract
The convergence of Artificial Intelligence (AI) and Machine Learning (ML) with DevSecOps represents a groundbreaking paradigm shift in software development practices. This paper explores the transformative impact of integrating AI/ML technologies into the DevSecOps framework, revolutionizing the way software is designed, developed, and secured. Through a comprehensive analysis of current trends, challenges, and opportunities, the paper elucidates the key strategies and best practices for leveraging AI/ML in DevSecOps. Topics addressed include automated threat detection, predictive analytics for vulnerability management, intelligent automation, and the ethical considerations surrounding AI/ML deployment in security-sensitive environments. By embracing this convergence, organizations can enhance their security posture, accelerate software delivery, and foster a culture of continuous improvement. Case studies and real-world examples are presented to illustrate the practical applications and benefits of AI/ML in transforming DevSecOps practices.
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Cites
Artificial hallucinations in ChatGPT: implications in scientific writing
Alkaissi, H., & McFarlane, S. I. (2023) · Cureus · doi:10.7759/cureus.35179 · Cited by 1,540
Denny, P., Kumar, V., & Giacaman, N. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569823 · Cited by 287
An Empirical Study of the Non-Determinism of ChatGPT in Code Generation
Ouyang, S., Zhang, J. M., Harman, M., & Wang, M. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3697010 · Cited by 258
Self-collaboration Code Generation via ChatGPT
Dong, Y., Jiang, X., Jin, Z., & Li, G. (2023) · arXiv · doi:10.48550/arxiv.2304.07590 · Cited by 256
Automatic Code Summarization via ChatGPT: How Far Are We?Preprint
Sun, W.-S., Fang, C., You, Y., Miao, Y., Liu, Y., Li, Y., Deng, G.-L., Huang, S., Chen, Y., Zhang, Q.-J., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2305.12865 · Cited by 121
Jailbreaking ChatGPT via Prompt Engineering: An Empirical StudyPreprint
Liu, Y., Deng, G., Xu, Z., Li, Y., Zheng, Y., Zhang, Y., Zhao, L., Zhang, T., Wang, K., & Liu, Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.13860 · Cited by 109
Piloting Copilot, Codex, and StarCoder2: Hot temperature, cold prompts, or black magic?
Döderlein, J.-B., Kouadio, N. H., Acher, M., Khelladi, D. E., & Combemale, B. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112562 · Cited by 67
Enhancing Human-Robot Collaboration in Industry 4.0 with AI-driven HRI
Vemuri, N. V. N. (2023) · Power System Technology · doi:10.52783/pst.196 · Cited by 40
Smart Farming Revolution: Harnessing IoT for Enhanced Agricultural Yield and Sustainability
Vemuri, N., Thaneeru, N., & Tatikonda, V. M. (2023) · Journal of Knowledge Learning and Science Technology · doi:10.60087/jklst.vol2.n2.p148 · Cited by 35
AI-assisted coding: Experiments with GPT-4Preprint
Poldrack, R. A., Lu, T., & Beguš, G. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.13187 · Cited by 32
Research works printed but not linked: 5
- Naveen Pakalapati, Bhargav Kumar Konidena, Ikram Ahamed Mohamed, Unlocking the Power of AI/ML in DevSecOps: Strategies and Best Practices , Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online): Vol. 2 No. 2 (2023): Innovative Pedagogical Approaches and Technological Integration in Modern Education
- Monish Katari, Selvakumar Venkatasubbu, Gowrisankar Krishnamoorthy, Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging , Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online): Vol. 2 No. 3 (2023): Pioneering Approaches in Health And Wellness: Exploring Biomedical Advances and Lifestyle Interventions
- Sai Mani Krishna Sistla, Bhargav Kumar Konidena, IoT-Edge Healthcare Solutions Empowered by Machine Learning , Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online): Vol. 2 No. 2 (2023): Innovative Pedagogical Approaches and Technological Integration in Modern Education
- Gowrisankar Krishnamoorthy, Sai Mani Krishna Sistla, Leveraging Deep Learning for Climate Change Prediction Models : A Dive into Cutting-Edge Techniques , Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online): Vol. 2 No. 2 (2023): Innovative Pedagogical Approaches and Technological Integration in Modern Education
- Naveen Pakalapati, Jawaharbabu Jeyaraman, Sai Mani Krishna Sistla, Building Resilient Systems: Leveraging AI/ML within DevSecOps Frameworks , Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online): Vol. 2 No. 2 (2023): Innovative Pedagogical Approaches and Technological Integration in Modern Education
Cited by
Sorted by citations
Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review
Mohammed, K., Shanmugam, B., & El-Den, J. (2025) · Technologies · doi:10.3390/technologies13120548 · Cited by 8
Metadata
- Authors
- Pakalapati, N., Venkatasubbu, S., & Sistla, S. M. K.
- Venue
- Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)
- Publisher
- Open Knowledge
- Published
- 2023-08-12
- Volume
- 2
- Issue
- 2
- Pages
- 189-212
- ISSN
- 2959-6386 (ISSN-L)
- OpenAlex
- W4392124076
- Cited by
- 6
- References
- 12
- Citations by year
- 2025: 5 · 2024: 1
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Knowledge Learning and Science Technology (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Web Application Security Vulnerabilities
- Keywords
- DevSecOps, machine learning for security, automated threat detection, vulnerability management, predictive analytics, intelligent automation
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- The paper
- Pakalapati, N., Venkatasubbu, S., & Sistla, S. M. K. (2023). The Convergence of AI/ML and DevSecOps: Revolutionizing Software Development. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online). https://doi.org/10.60087/jklst.vol2.n2.p212
