Vol. 1 No. 1 (2026): January 2026
Artificial Intelligence : Technology

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

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  • Rajesh Lingam
Rajesh Lingam
Senior Software Engineer, Acton, Massachusetts, 01720, United States of America.
DOI: https://doi.org/10.5281/zenodo.18439428

Published 2026-01-31

Keywords

  • Zero-Trust Architecture,
  • Secure DevOps Automation,
  • Enterprise AI Systems,
  • Anomaly Detection,
  • Autoencoder,
  • XGBoost,
  • DevSecOps
    • ...More
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How to Cite

Rajesh Lingam. (2026). Zero-Trust Architectures for Secure DevOps Automation in Enterprise AI Systems. Milestone Transactions on Artificial Intelligence, 1(1), 18–33. https://doi.org/10.5281/zenodo.18439428

Abstract

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.

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