AI-DRIVEN ANOMALY DETECTION FOR PROACTIVE CYBERSECURITY AND DATA BREACH PREVENTION

In an era of escalating cyber threats, traditional reactive security measures often fail to prevent data breaches andinsider attacks. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools forproactive cybersecurity, enabling the identification of anomalies in real-time network traffic and system behaviour.These technologies leverage advanced algorithms and vast datasets to uncover subtle deviations indicative ofpotential breaches or insider threats. This paper investigates the deployment of AI-driven anomaly detectionsystems, emphasizing their ability to monitor and analyse complex network environments. ML models, includingsupervised, unsupervised, and reinforcement learning approaches, are evaluated for their efficacy in detectingirregular patterns that traditional methods may overlook. Key focus areas include real-time traffic analysis, userbehaviour analytics, and the identification of unknown threats. Furthermore, the study highlights the role of featureengineering, neural networks, and clustering algorithms in enhancing anomaly detection accuracy. Case studiesfrom high-risk sectors such as financial services, healthcare, and critical infrastructure demonstrate how AI toolshave thwarted cyberattacks, preserved data integrity, and ensured business continuity. In this research, challengessuch as minimizing false positives, ensuring algorithm transparency, and addressing ethical concerns are exploredin detail. The integration of AI into cybersecurity frameworks offers a proactive approach to breach prevention,reducing response times and enabling dynamic threat mitigation. This paper concludes that AI-driven anomalydetection is an indispensable component of modern cybersecurity strategies, fostering robust data protection inincreasingly complex and high-stakes digital environments