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paperJune 2026Unreviewed

Hybrid ML-LLM Pipeline for Non-Governance IT Audits

Kaung Myat Naing, Talha Ali, Mohammed Ouannass

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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

Categories

Monitoring & Detection

Cite

@misc{naing2026hybrid,
  title = {{Hybrid ML-LLM Pipeline for Non-Governance IT Audits}},
  author = {Kaung Myat Naing and Talha Ali and Mohammed Ouannass},
  year = {2026},
  month = jun,
  doi = {10.4018/979-8-3373-8252-4.ch009},
  url = {https://doi.org/10.4018/979-8-3373-8252-4.ch009}
}