Building Resilient SMEs: Harnessing Large Language Models for Cyber
  Security in Australia
Benjamin Kereopa-Yorke
The escalating digitalisation of our lives and enterprises has led to a
parallel growth in the complexity and frequency of cyber-attacks. Small and
medium-sized enterprises (SMEs), particularly in Australia, are experiencing
increased vulnerability to cyber threats, posing a significant challenge to the
nation's cyber security landscape. Embracing transformative technologies such
as Artificial Intelligence (AI), Machine Learning (ML) and Large Language
Models (LLMs) can potentially strengthen cyber security policies for Australian
SMEs. However, their practical application, advantages, and limitations remain
underexplored, with prior research mainly focusing on large corporations. This
study aims to address this gap by providing a comprehensive understanding of
the potential role of LLMs in enhancing cyber security policies for Australian
SMEs. Employing a mixed-methods study design, this research includes a
literature review, qualitative analysis of SME case studies, and a quantitative
assessment of LLM performance metrics in cyber security applications. The
findings highlight the promising potential of LLMs across various performance
criteria, including relevance, accuracy, and applicability, though gaps remain
in areas such as completeness and clarity. The study underlines the importance
of integrating human expertise with LLM technology and refining model
development to address these limitations. By proposing a robust conceptual
framework guiding the effective adoption of LLMs, this research aims to
contribute to a safer and more resilient cyber environment for Australian SMEs,
enabling sustainable growth and competitiveness in the digital era.