Evaluating Human-Machine Interaction Paradigms for Effective Human-Artificial Intelligence Collaboration in Cybersecurity

Abstract

This paper examines the efficacy of various Human-Machine Interaction (HMI) paradigms in enhancing cybersecurity practices through human-artificial intelligence (AI) collaboration. As cyber threats grow increasingly sophisticated, organisations are turning to AI to bolster their defence mechanisms, threat detection, incident response, and overall security management. Six HMI paradigms are analyzed: Humans in the Loop (HITL), Humans on the Loop (HOTL), Humans out of the Loop (HOOTL), Humans alongside the Loop (HATL), Humans-in-command (HIC), and Coactive Systems. HITL is about active direct human intervention while HOOTL emphasises autonomous AI operations. HOTL balances AI autonomy with human oversight. In HATL, AI and humans work simultaneously on different tasks, whereas in Coactive Systems, humans and AI collaborate equally and interdependently. Lastly, in HIC, humans are the final decision-makers and can override AI decisions. The strengths and weaknesses of the six HMI paradigms are determined by evaluating their key components against high-level cybersecurity practices, leveraging the advanced capabilities of ChatGPT-40. The findings underscore the need for a hybrid approach that flexibly integrates multiple paradigms to optimize performance. Recommendations for practical implementation are provided, along with an outline of areas for future research, including real-world testing and the exploration of emerging AI advancements.