International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Implementation of AI-Powered Threat Detection in Devsecops Workflows for Enhancing Code Security and Real-Time Risk Assessment through Predictive Anomaly Recognition |
|---|---|
| ABSTRACT | The integration of artificial intelligence (AI) into DevSecOps workflows represents a significant shift in contemporary software development, enabling proactive threat detection and real-time risk assessment to mitigate code vulnerabilities. This study explores the implementation of AI-driven predictive anomaly recognition systems within continuous integration and continuous deployment (CI/CD) pipelines. Employing a mixed-methods approach, including simulation-based experiments on public datasets such as the Kaggle Code Vulnerabilities Dataset and UNSW-NB15, the research evaluates machine learning models, including random forests and long short-term memory (LSTM) networks, for anomaly detection. The results indicate a 25% improvement in detection accuracy and a 40% reduction in false positives compared to traditional static analysis tools, while real-time risk scoring enhances decision-making across development cycles. Reproducibility is ensured through the use of open-source frameworks such as TensorFlow and scikit-learn. The findings highlight the transformative potential of AI in enabling secure and agile DevSecOps practices, addressing gaps in predictive security capabilities, and recommending hybrid modeling approaches for scalable deployment. This study contributes to both theoretical advancements in cybersecurity and practical guidelines for industry adoption, emphasizing ethical AI deployment to balance innovation with security. |
| TITLE | |
| AUTHOR | SUPRITH ANCHALA Manager (Delivery), Qualitest Group, Remote, Texas, United States |
| VOLUME | 165 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1301169 |
| pdf/169_Implementation of AI-Powered Threat Detection in Devsecops Workflows for Enhancing Code Security and Real-Time Risk Assessment through Predictive Anomaly Recognition.pdf | |
| KEYWORDS | |
| References | [1] Binbeshr, F., & Imam, M. (2024). Comparative analysis of AI-driven security approaches in DevSecOps. arXiv. [2] Varun Kumar Tambi, Nishan Singh (2023). Developments and Uses of Generative Artificial Intelligence and Present Experimental Data on the Impact on Productivity Applying Artificial Intelligence that is Generative. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), 12(10). [3] Pankit Arora & Sachin Bhardwaj (2023). Examining Cloud Computing Data Confidentiality Techniques to Achieve Higher Security in Cloud Storage. International Journal Of Multidisciplinary Research In Science, Engineering and Technology (IJMRSET), 6(10). [4] Sidharth Sharma (2023). Ai-driven anomaly detection for advanced threat detection. [5] Fortinet. (2024). Top cybersecurity statistics. [6] Guda, D. P. (2024). AI-Driven Threat Detection in DevSecOps Pipelines. International Journal of Intelligent Systems and Applications in Engineering, 12(23s). [7] Varun Kumar Tambi (2022). REAL-TIME COMPLIANCE MONITORING IN BANKING OPERATIONS USING AI. INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR), 9(9), 35-47. [8] Lombardi, F., & Fanton, A. (2023). From DevOps to DevSecOps. Journal of Systems and Software, 198, 111–125. [9] Mankotia, A. (2024). Impact of AI on DevOps and DevSecOps. International Journal of Management, IT & Engineering, 14(7). [10] Varun Kumar Tambi (2021). NATURAL LANGUAGE UNDERSTANDING MODELS FOR PERSONALIZED FINANCIAL SERVICES. International Journal of Current Engineering and Scientific Research, 8(1):1-11. [11] Pakalapati, N., et al. (2023). The Convergence of AI/ML and DevSecOps. Journal of Knowledge Learning and Science Technology, 2(2), 189-212. [12] Sidharth Sharma (2022). Enhancing Generative AI Models for Secure and Private Data Synthesis. [13] PurpleSec. (2024). 2024 Cybersecurity Statistics. [14] Schummer, P., et al. (2024). Machine learning-based network anomaly detection. AI, 5(4), 2967–2983. [15] Pankit Arora & Sachin Bhardwaj (2023). Techniques to Implement Security Solutions and Improve Data Integrity and Security in Distributed Cloud Computing. International Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET), 6(6). [16] Statista. (2024). Number of data breaches in the U.S. [17] UpGuard. (2024). Biggest Data Breaches in US History. [18] Varun Kumar Tambi, Nishan Singh (2022). Creating J2EE Application Development Using a Pattern-based Environment. International Journal of Innovative Research in Computer and Communication Engineering, 10(11). [19] Yuan, L.-P., et al. (2021). Recompose event sequences vs. predict next events. ACM AsiaCCS, 336–348. [20] Sidharth Sharma (2022). Zero trust architecture: a key component of modern cybersecurity frameworks. [21] Zettler, K. (2022). The DevSecOp tools that secure DevOps workflows. IEEE Software, 39(4), 45-52. [22] Black Duck. (2023). AI in DevSecOps report. [23] CM Alliance. (2024). Top 10 Biggest Cyber Attacks of 2024. [24] Datadog. (2023). Cloud security roundup: DevSecOps. [25] Gartner. (2023). AI in cybersecurity forecast. [26] OWASP. (2020). Top 10 vulnerabilities. [27] NIST. (2022). Framework for improving critical infrastructure cybersecurity. |
