The Impact of Artificial Intelligence on Cybersecurity - Sabah Abdellatif Hassan Ahmed (المجلة الدولية للحاسبات والمعلوماتية, 2024)
The aim of this research is to examine the structure of the impact of AI technology on improving cybersecurity.  Also, this research focuses on using machine learning and big data analysis techniques to enhance the ability to better detect and respond to cyber threats. This research follows a qualitative methodology to meet the core research objectives of assessing the impact of Artificial Intelligence on Cybersecurity by reviewing the previous studies on this field. This research focuses on using machine learning and big data analysis techniques to enhance the ability to better detect and respond to cyber threats. The comprehensive analysis of over 20 studies published between 2015-2024 offers valuable insights into the current state and impact of AI in enhancing cybersecurity policies and practices. The findings of the research revealed that artificial intelligence and machine learning significantly expand capabilities for cyber threat detection, incident alerting, and automated response through adaptive pattern recognition across diverse monitoring sources like network traffic, endpoint behaviors and security information feeds, performed at machine scale. By constantly retraining on evolving attack trends and benign usage shifts, AI holds immense potential enhancing protection. However, sizable gaps observed between touted expectations versus measured impact today highlight obstacles of practical integration like monitoring overheads, skills shortages, result interpretability, human-AI teaming dynamics, and adversarial manipulations that could undermine or reverse security aims. While transformative upside exists long-term, pragmatic roadmaps addressing these formidable challenges using best practices around responsible AI governance appear essential to maximize gains while minimizing unintended consequences of well-intentioned systems interacting with ever stealthier threats amid zones of high operational and ethical ambiguity.The research recommends using machine learning and big data analysis techniques to enhance the ability to better detect and respond to cyber threats.