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This paper integrates existing literature on the subject that converges with ongoing discussions concerning cyber security and machine learning. The paper advocates for the transformative potential of automation in compliance processes, offering increased efficiency and reliability. This study explores the intersection of cybersecurity and machine learning, focusing on how machine learning can independently analyze documents to establish whether they meet the prescribed evidence standards through programmed algorithms. Cybersecurity compliance evaluations typically require stakeholders to review voluminous documentation manually. Using machine learning to automate this compliance assessment process brings in a lot of advantages in terms of timesaving, cost efficiency, and improved accuracy. This paper's main objective is to review and assess previously proposed models or datasets and their outcomes within the domain state; as a result, it offers a thorough summary of the latest machine learning-based automation algorithms for compliance.

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Automating Compliance Evidence Extraction with Machine Learning

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Sunday O. Olatunji,
  • Abdulrahman S. Almuhaidib,
  • Abdulaziz S. Alhuwaishil,
  • Ahmad S. Alarfaj,
  • Khalifah W. Alzwaimel,
  • Hassan A. Alsaleem,
  • Abdulaziz A. Alsudais,
  • Naeem M. Owaida,
  • Fahd A. Alhaidari,
  • Sghaier R. Chabani

摘要

This paper integrates existing literature on the subject that converges with ongoing discussions concerning cyber security and machine learning. The paper advocates for the transformative potential of automation in compliance processes, offering increased efficiency and reliability. This study explores the intersection of cybersecurity and machine learning, focusing on how machine learning can independently analyze documents to establish whether they meet the prescribed evidence standards through programmed algorithms. Cybersecurity compliance evaluations typically require stakeholders to review voluminous documentation manually. Using machine learning to automate this compliance assessment process brings in a lot of advantages in terms of timesaving, cost efficiency, and improved accuracy. This paper's main objective is to review and assess previously proposed models or datasets and their outcomes within the domain state; as a result, it offers a thorough summary of the latest machine learning-based automation algorithms for compliance.