Artificial Intelligence in Cybersecurity: A Comprehensive Review of AI-Powered Threat Detection, Cyberattack Prevention, and Information Security

The rapid expansion of digital technologies, cloud computing, interconnected networks, and online services has significantly increased the complexity of modern cybersecurity challenges. Organizations increasingly face sophisticated cyber threats, including malware infections, ransomware, phishing campaigns, unauthorized network intrusions, insider threats, and advanced persistent threats (APTs). Traditional cybersecurity mechanisms, which primarily depend on predefined rules, signature-based detection, and manual security monitoring, often struggle to identify previously unknown attacks and respond effectively to rapidly evolving threats. Artificial intelligence (AI), particularly machine learning (ML), deep learning (DL), natural language processing (NLP), and behavioral analytics, has emerged as a promising approach for strengthening cybersecurity capabilities.This review examines the application of AI-driven technologies in modern cybersecurity, emphasizing intelligent threat detection, cyberattack prevention, and information security. It explores the role of machine learning and deep learning in network intrusion detection, malware classification, phishing identification, anomaly detection, behavioral threat analysis, and automated incident response. Furthermore, the review investigates emerging developments in explainable artificial intelligence (XAI), adversarial machine learning, federated learning, and generative AI, highlighting their implications for developing more adaptive and resilient security infrastructures. Recent academic research published in 2024 and 2025 is considered to evaluate technological developments, practical applications, and emerging limitations.The review identifies several important challenges affecting the practical adoption of AI-based cybersecurity systems, including data quality, model interpretability, false-positive detection, privacy concerns, adversarial manipulation, computational requirements, and the limited generalizability of models across different network environments. The analysis suggests that AI should complement, rather than completely replace, conventional security controls and human cybersecurity expertise. A layered cybersecurity architecture integrating AI-based detection, explainable decision support, continuous model monitoring, and human-supervised incident response may provide a more reliable approach to protecting modern digital environments. Finally, the paper identifies research opportunities related to adversarial robust AI, privacy-preserving collaborative learning, trustworthy generative AI, and autonomous cyber defense.