Artificial Intelligence (AI) in Cybersecurity and Inhibitors to AI Adoption

Abstract

This review examines Artificial Intelligence (AI) in Cybersecurity. The purpose of this examination is to identify and contrast AI advancements in Cybersecurity, areas for improvements, and inhibitors to advancing the use of AI for Cybersecurity. AI has seen exponential growth and advancement in innovation and automation in recent years and security professionals are benefiting from implementing and using AI based Cybersecurity tools. There are implementation concerns for data privacy, security vulnerabilities and AI biases resulting from training data AI is dependent on for learning. There are also trust issues between humans and machines. This study aims to provide a structured evaluation of AI advancements in Cybersecurity and AI adoption concerns. It does this by reviewing current and relevant academic articles from January 2022 to January 2024 on AI in Cybersecurity and recommend ideas for further studies. The comparison and contrast revealed advancements in AI Dataset are needed, such as Realtime datasets and Privacy Datasets, to teach AI about privacy. Improvements in Explainable AI (XAI) is needed to provide algorithm transparency and provide insights into AI decision-making process and outcomes. A key adoption issue is Data Privacy, closely followed by performance and accuracy of AI algorithms. Trust is another key issue at the centre of most studies. The review also identifies future research opportunities in emerging cybersecurity application areas, AI components and awareness of AI inhibitors for the successful adoption of AI-based cybersecurity.

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