Citation:

Selva Kumar Ranganathan, 2022. "AI-Augmented DevSecOps: Enhancing Security through Predictive Intelligence", ESP Journal of Engineering & Technology Advancements 2(3): 172-177.

Abstract:

As the velocity of software delivery accelerates in the age of agile development and cloud-native architectures, ensuring robust security has become a critical challenge. Traditional security models often struggle to keep pace with the speed, scale, and complexity of modern CI/CD workflows. This research investigates the integration of Artificial Intelligence (AI) into DevSecOps to transition from reactive defenses to predictive, intelligent security mechanisms.By leveraging AI techniques such as supervised learning, anomaly detection, and time-series modeling, organizations can proactively identify vulnerabilities, detect behavioral anomalies, and enforce adaptive controls in real time. AI-driven systems can continuously process vast streams of security telemetry including code commits, build logs, runtime behaviors, and threat intelligence feeds to identify subtle patterns indicative of emerging threats. This enables not only earlier detection but also autonomous mitigation, reducing reliance on manual intervention and lowering operational burden.This paper presents a comprehensive framework for embedding AI within the DevSecOps lifecycle, ensuring security is integrated as a continuous, intelligent process from code commit to production deployment. The methodology involves multi-phase implementation: data acquisition, AI model training, and real-time pipeline integration. Experimental evaluations demonstrate measurable improvements in key security metrics, including detection accuracy, mean time to detect (MTTD), and mean time to respond (MTTR), while also significantly reducing false positive rates.Furthermore, the study explores the challenges of model drift, integration complexity, explainability, and regulatory compliance, offering practical solutions for sustainable deployment. By aligning the strengths of AI with the principles of DevSecOps, this research highlights a path toward building secure, adaptive, and self-evolving software systems that are resilient against both known and novel cyber threats.

References:

[1] Brown, J., & Wilson, A. (2023). AI in Cybersecurity: The New Frontier. Cybersecurity Press.

[2] Chen, M., & Zhao, Y. (2022). Predictive Threat Intelligence in DevOps. Journal of DevOps Security, 14(2), 45–63.

[3] NIST. (2021). DevSecOps practices. https://www.nist.gov/publications/devsecops-practices

[4] Sharma, P., & Patel, R. (2020). Integrating Machine Learning into CI/CD pipelines. ACM Software Engineering Notes, 45(3), 12–19.

Keywords:

Artificial Intelligence (AI), Devsecops, Predictive Intelligence, Supervised Learning, Anomaly Detection, Time-Series Modeling, Threat Detection, Security Automation, MTTD (Mean Time To Detect), MTTR (Mean Time To Respond), Integration Complexity, Intelligent Security Frameworks, Adaptive Security Systems, Scalable Devsecops Solutions.