Application of Machine Learning Algorithms for Anomaly Detection in Cybersecurity Threat Mitigation

The integration of Artificial Intelligence (AI) into cybersecurity has transformed the landscape of threat detection, analysis, and mitigation. As cyber-attacks become increasingly sophisticated and evasive, traditional rule-based defences are no longer sufficient to identify zero-day exploits and advanced persistent threats. AI-driven approaches, leveraging machine learning and deep learning, enable proactive anomaly detection, behavioural modelling, and predictive analytics that enhance both the accuracy and agility of cyber defence mechanisms.This paper provides a comprehensive examination of AI applications in cybersecurity, spanning anomaly detection, automated incident response, and adaptive defence frameworks. It also emphasizes the emerging role of AI in vulnerability management, where predictive modelling, natural language processing, and automated remediation are used to identify, prioritize, and mitigate vulnerabilities before they can be exploited. A real-world case study of Panasonic’s VERZEUSE™ platform is presented to illustrate the industrial implementation of AI-enhanced cybersecurity. The platform exemplifies how AI-based predictive analytics, threat intelligence integration, and continuous monitoring can strengthen risk management and compliance in complex IT and IoT ecosystems.The findings demonstrate that AI substantially improves detection accuracy, response speed, and proactive defence capabilities. However, challenges related to data quality, model robustness, interpretability, and ethical deployment must be addressed to ensure trustworthy adoption. The study concludes that the future of cybersecurity depends on harmonizing human expertise with adaptive AI systems to achieve resilient, self-learning defence frameworks.