Human-Artificial Intelligence Teaming Model in Cybersecurity

Abstract

The escalating complexity and sophistication of cybersecurity threats necessitate innovative approaches. One such approach is adopting autonomous artificial intelligence (AI) agents in collaboration with humans. This paper introduces a hybrid Human-AI Teaming (HAIT) model designed to optimise cybersecurity operations across multiple domains by integrating diverse Human-Machine Interaction (HMI) paradigms. Leveraging insights from previous research on HMI effectiveness in cybersecurity (see paper entitled Evaluating Human-Machine Interaction Paradigms for Effective Human-Artificial Intelligence Collaboration in Cybersecurity), the model strategically combines approaches such as Human in the Loop, Human on the Loop, and Coactive Systems to address the multifaceted nature of AI-driven cybersecurity tasks. The proposed HAIT model comprises five key elements: a Decision-Making Matrix, Dynamic Paradigm Allocation, Task-Specific Customisation, Feedback Loops, and Interoperability. These components work together to enhance adaptability, efficiency, and resilience in the face of evolving cyber threats. The paper explores the implications of this model for cybersecurity practitioners. It outlines a phased implementation strategy and identifies avenues for future research, such as enhancing the model's contextual understanding through advanced AI and machine learning tools.