AI for Database Security Anomaly Detection: Leveraging Machine Learning for Real-Time Threat Identification

Abstract—With the exponential growth of digital data, database security has become a critical concern for orga- nizations across industries. Traditional rule-based intrusion detection methods struggle to detect evolving and sophisticated threats. This research investigates the application of artificial intelligence (AI) and machine learning (ML) to detect anoma- lies in a real-time database. Using access logs, transaction patterns, and user behavior analytics, ML models can identify anomalies and potential security breaches with higher accuracy and adaptability. The proposed approach emphasizes explain- ability, compliance, and adaptability in dynamic database environments.