The fusion of artificial intelligence (AI) with cybersecurity represents a paradigm shift in our efforts to safeguard digital assets against a dynamic threat landscape. This manuscript comprehensively analyses AI's transformative role in cybersecurity, covering foundational principles, advanced methodologies, and ethical considerations. This article begins with exploring fundamental AI techniques such as machine learning and natural language processing. The manuscript delineates their applications in bolstering threat detection, vulnerability analysis, and incident response. Traditional approaches to vulnerability analysis are juxtaposed with AI-driven methodologies, highlighting the efficacy of automated scanning, threat prioritization, and adaptive risk assessment. Moreover, the manuscript delves into the pivotal role of AI-driven automation in expediting incident response, minimizing human error, and fortifying overall security postures. Ethical and privacy concerns surrounding AI deployment in cybersecurity are carefully examined, emphasizing the importance of responsible decision-making, privacy protection, and transparency. Looking ahead, emerging trends such as adversarial machine learning and zero trust security present promising avenues for further exploration, offering opportunities to enhance digital resilience against evolving threats.
Sontan et al. (2024) studied this question.