Hybrid ML-LLM Pipeline for Non-Governance IT Audits

Kaung Myat Naing, Talha Ali, Mohammed Ouannass

Abstract

Organizations outside formal governance frameworks often lack cybersecurity audit tools, making anomaly detection and risk evaluation difficult. This paper presents an AI-enhanced auditing framework for non-governance IT environments. Using the UNSW-NB15 dataset, we evaluate four machine-learning models: Isolation Forest, Logistic Regression, Gradient Boosting, and XGBoost, identifying complementary strengths that motivate a two-stage filter for suspicious network flows. Flagged flows ar

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Cite This Resource

@article{llmsec202600203,
  title = {Hybrid ML-LLM Pipeline for Non-Governance IT Audits},
  author = {Kaung Myat Naing and Talha Ali and Mohammed Ouannass},
  year = {2026},
  doi = {10.4018/979-8-3373-8252-4.ch009},
  url = {https://doi.org/10.4018/979-8-3373-8252-4.ch009},
}