Building Resilient SMEs: Harnessing Large Language Models for Cyber Security in Australia
Benjamin Kereopa-Yorke

TL;DR
This paper explores how Large Language Models can enhance cyber security policies for Australian SMEs, highlighting their potential benefits, current limitations, and the importance of integrating human expertise for improved resilience.
Contribution
It provides a comprehensive analysis of LLM applications in SME cyber security, including a conceptual framework for effective adoption tailored to Australian SMEs.
Findings
LLMs show high relevance, accuracy, and applicability in cyber security tasks.
Gaps identified in completeness and clarity of LLM outputs.
Integration of human expertise is crucial for effective LLM deployment.
Abstract
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,…
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Taxonomy
TopicsCybercrime and Law Enforcement Studies · Cybersecurity and Cyber Warfare Studies · Information and Cyber Security
