Abstract

ABSTRACT Cybersecurity is a prime concern today for businesses due to the rapid increase in cyber-attacks, inadequate security controls, stricter regulations, and lack of security awareness among the workforce. A robust cybersecurity policy can address the security needs of businesses with directives on acceptable actions and behavior. Developing such a policy for business entities requires adequate skill and knowledge. Reviewing voluminous policy texts to identify best practices is also time-consuming. Therefore, the objective of this study is to provide a natural language processing (NLP)-based methodology that can quickly identify the significant topics and themes from the cybersecurity policies of leading global businesses. Text mining and Latent Dirichlet Allocation-based topic modeling technique have been used on cybersecurity policy-related textual contents obtained from 10 leading Fortune Global 500-listed business organizations and the extracted output is then mapped to the globally popular cybersecurity standard ISO/IEC 27,001:2022 to determine the relevancy. The study reveals significant topics and themes that can be used for the development or enhancement of cybersecurity policies to protect businesses from cyber threats.

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