AI-Augmented DevSecOps Pipelines for Secure and Scalable Service-Oriented Architectures in Cloud-Native Systems

Cloud-native architectures face escalating security challenges that traditional approaches cannot address at scale. This paper presents an AI-augmented DevSecOps framework integrating machine learning models into security pipelines for realtime threat detection and automated response. The framework...

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Bibliographic Details
Published in:2025 IEEE International Conference on Service-Oriented System Engineering (SOSE) pp. 79 - 84
Main Author: Mittal, Akshay
Format: Conference Proceeding
Language:English
Published: IEEE 21.07.2025
Subjects:
Adaptation models
Anomaly detection
ci/cd pipelines
cloud-native security
devsecops
Faces
kubernetes
Machine learning
Microservice architectures
microservices
Pipelines
Security
Service-oriented systems engineering
Threat assessment
Training
ISSN:2642-6587
Online Access:Get full text
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Summary:Cloud-native architectures face escalating security challenges that traditional approaches cannot address at scale. This paper presents an AI-augmented DevSecOps framework integrating machine learning models into security pipelines for realtime threat detection and automated response. The framework achieves 95% attack detection rates with sub-2 second latency at 10 k events/sec. Key contributions include LSTM-based threat detection embedded in CI/CD workflows, adaptive model training with 98% accuracy retention over 6 months, and complete opensource implementation. Experimental validation across multiple attack scenarios demonstrates effectiveness while maintaining operational efficiency in hybrid Kubernetes-serverless environments.
ISSN:2642-6587
DOI:10.1109/SOSE67019.2025.00014