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This is a Quantitative Study study.

October 31, 2025Journal of information technology, cybersecurity, and artificial intelligence.0 citationsOpen Access

MACHINE LEARNING (ML) TO EVALUATE GOVERNANCE, RISK, AND COMPLIANCE (GRC) RISKS ASSOCIATED WITH LARGE LANGUAGE MODELS (LLMs)

Key Points

Machine learning predicts GRC risk levels in interactions with large language models, guiding compliance efforts.
Key features include temperature, compliance, and risk metrics to assess LLM performance in organizations.
Analysis utilizes Azure OpenAI Service logs to construct a comprehensive risk dataset from user interactions.
Highlights the importance of effective governance in AI, especially regarding compliance in various industries.

Abstract

In today’s AI-driven digital world, Governance, Risk, and Compliance (GRC) has become vital for organizations as they leverage AI technologies to drive business success and resilience. GRC represents a strategic approach that helps organization using Large Language Models (LLMs) automation tasks and enhances customer service, while maintaining the regulatory complexity across various industries and regions. This paper explores a machine learning approach to evaluate Governance, Risk, and Compliance (GRC) risks associated with Large Language Models (LLMs). It utilizes Azure OpenAI Service logs to construct a representative dataset, with key features including responseₜimeₘs, modelₜype, temperature, tokensᵤsed, isₗogged, dataₛensitivity, complianceflag, biasₛcore, and toxicityₛcore. These features are used to train a model that predicts GRC risk levels in LLM interactions, enabling organizations to improve efficiency, foster innovation, and deliver customer value, while maintaining compliance and regulatory requirements.

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