AI-powered threat detection: Opportunities and limitations in modern cyber defense

Artificial intelligence (AI) and machine learning (ML) have become critical components of modern cybersecurity strategies, offering dynamic capabilities for detecting, analyzing, and mitigating cyber threats. This review synthesizes existing literature to explore how AI and ML technologies are being applied in cyber threat detection, focusing on their operational integration, effectiveness, and limitations. The study draws on 43 referenced sources, including peer-reviewed journal articles, technical whitepapers, vendor documentation, and authoritative blogs, to provide a comprehensive overview of the field. Findings highlight that AI enhances threat detection through real-time data analysis, reduces false positives, and uses predictive modeling and adaptive learning. These technologies enable more proactive and scalable defense mechanisms compared to traditional rule-based systems. However, challenges persist, including the opacity of black-box models, vulnerability to adversarial attacks, data quality issues, and the lack of standard evaluation frameworks. Regulatory concerns and the need for human oversight further complicate widespread deployment. The review concludes that while AI significantly augments cyber defense capabilities, it is not a standalone solution. For AI to be effectively and ethically integrated into cybersecurity, it must be transparent, explainable, and aligned with organizational and regulatory goals. The study emphasizes the importance of explainable AI, robust datasets, and interdisciplinary collaboration in shaping the next generation of secure and trustworthy AI-driven defense systems.