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The escalating digitalisation of our lives and enterprises has led to aparallel growth in the complexity and frequency of cyber-attacks. Small andmedium-sized enterprises (SMEs), particularly in Australia, are experiencingincreased vulnerability to cyber threats, posing a significant challenge to thenation's cyber security landscape. Embracing transformative technologies suchas Artificial Intelligence (AI), Machine Learning (ML) and Large LanguageModels (LLMs) can potentially strengthen cyber security policies for AustralianSMEs. However, their practical application, advantages, and limitations remainunderexplored, with prior research mainly focusing on large corporations. Thisstudy aims to address this gap by providing a comprehensive understanding ofthe potential role of LLMs in enhancing cyber security policies for AustralianSMEs. Employing a mixed-methods study design, this research includes aliterature review, qualitative analysis of SME case studies, and a quantitativeassessment of LLM performance metrics in cyber security applications. Thefindings highlight the promising potential of LLMs across various performancecriteria, including relevance, accuracy, and applicability, though gaps remainin areas such as completeness and clarity. The study underlines the importanceof integrating human expertise with LLM technology and refining modeldevelopment to address these limitations. By proposing a robust conceptualframework guiding the effective adoption of LLMs, this research aims tocontribute to a safer and more resilient cyber environment for Australian SMEs,enabling sustainable growth and competitiveness in the digital era.

Building Resilient SMEs: Harnessing Large Language Models for Cyber Security in Australia