Artificial Intelligence in Cybersecurity: A Bibliometric Analysis of Recent Advances and Emerging Trends

Cybersecurity faces mounting challenges as digital systems grow more complex and interconnected, necessitating innovative approaches to counter escalating threats. This study explores the evolving research landscape where artificial intelligence (AI), particularly machine learning and deep learning, emerges as a critical enabler for detecting, mitigating, and preventing cyberattacks. Utilizing a bibliometric analysis of over 10,000 publications from 2022 to 2024, this research examines key trends, thematic priorities, and collaborative dynamics shaping AI-driven cybersecurity solutions. Data collected from the Web of Science Core Collection was analyzed using Biblioshiny to map intellectual foundations, identify influential sources, and construct co-occurrence networks of core concepts. The findings reveal a field dominated by datacentric methodologies, with terms like "learning," "data," and "model" highlighting the integration of AI into anomaly detection, predictive analytics, and system defense. Geographic analysis underscores China's leadership in research productivity, complemented by strong international collaboration from countries like the United Kingdom and Australia. Thematic clusters highlight emerging concerns such as IoT security, privacy protection, and cloud resilience, emphasizing the ethical and practical implications of AI applications. This study demonstrates that AI is fundamentally reshaping cybersecurity, enabling scalable and adaptive solutions to meet evolving threats. Future research must continue fostering interdisciplinary collaboration and ethical innovation to ensure AI technologies remain robust and equitable tools in safeguarding digital ecosystems.