Zero-Trust Architectures for Secure DevOps Automation in Enterprise AI Systems

The rapid adoption of Enterprise Artificial Intelligence (AI) and automated DevOps pipelines has significantly increased the attack surface of modern software ecosystems. Traditional perimeter-based security mechanisms are inadequate for protecting highly dynamic, cloud-native, and AI-driven environments. Enterprise AI systems increasingly rely on automated Development Operation (DevOp) pipelines, which introduce complex security challenges that traditional perimeter-based models fail to address. Zero-Trust Architecture (ZTA) provides continuous verification and least-privilege access but often lacks adaptability in dynamic DevOps environments. This paper proposes a hybrid machine learning–based zero-trust framework for secure DevOps automation by integrating an Autoencoder for unsupervised anomaly detection with an XGBoost classifier for trust decision-making. The framework is evaluated on the TII-SSRC-23 dataset and compared with Logistic Regression (LR), Random Forest (RF), Support Vector Machine (VM), and Long Short-Term Memory (LSTM) models. Experimental results show that the proposed approach achieves superior performance, with 99% accuracy and a ROC-AUC of 0.99. The findings demonstrate that the hybrid model enables scalable, adaptive, and reliable security enforcement for enterprise AI-driven DevOps ecosystems.