Hybrid ML-LLM Pipeline for Non-Governance IT Audits

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Author(s): Kaung Myat Naing (Illinois Institute of Technology, USA), Talha Ali (Illinois Institute of Technology, USA)and Mohammed Ouannass (Illinois Institute of Technology, USA)
Copyright: 2026
Pages: 18
Source title: Examining Vulnerabilities and Adversarial Exploitation of AI and LLMs
Source Author(s)/Editor(s): Puya Pakshad (Illinois Institute of Technology, USA)and Marwan Omar (Illinois Institute of Technology, USA)
DOI: 10.4018/979-8-3373-8252-4.ch009

Keywords: Artificial Intelligence / Engineering Science Reference / Information Security & Privacy / Security & Forensics

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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 are aggregated into structured evidence and passed to a GPT-4-based large language model, which generates incident explanations, control mappings, and remediation suggestions. Results show the hybrid ML-LLM approach reduces audit workload, improves anomaly interpretation, and supports recommendations for private and small-scale IT systems. The study also highlights limitations including prompt sensitivity, false positives, and third-party AI risks. Overall, findings illustrate both the potential and challenges of deploying AI-driven audit pipelines to strengthen cybersecurity in non-governance settings.

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Hybrid ML-LLM Pipeline for Non-Governance IT Audits

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