Enhancing Cybersecurity with AI-Driven Anomaly Detection and Threat Analytics

This research explores how AI-driven anomaly detection enhances cybersecurity by identifying irregularities in user activity, network traffic, and system logs. Unlike traditional signature-based systems, AI models—particularly unsupervised and semi-supervised approaches—offer greater adaptability and precision in detecting emerging threats. The study evaluates these models’ performance in recognizing behavioral deviations while addressing challenges such as high false positives and inconsistent data quality. Findings show that AI-based analytics significantly improve threat detection accuracy and operational efficiency. However, the opaque nature of deep learning models limits interpretability, prompting the need for explainable AI (XAI) to support analysts’ understanding. The study concludes that AI-powered security analytics are essential for proactive threat management, with future work focusing on scalability, reducing false alerts, and enhancing visualization for effective human–AI collaboration.