JoWUA

Abstract

Enterprise database systems deal with large amounts of sensitive organizational information and, therefore, have to have powerful monitoring and protection policies to avoid unauthorized access, insider abuse, and possible data breaches. The conventional methods of monitoring databases are based on the traditional rule and preset thresholds, which restricts their powers in detecting complex anomalous queries as well as dynamic insider threats in the current business environment. Recent developments in machine learning and behavioral analytics showed that the capabilities to identify abnormal database operations have been improved, but most of the current methods do not have adaptive learning capabilities and support heterogeneous databases across platforms. This research study suggests an artificial intelligence-based monitoring system combining both database activity monitoring and behavioral anomaly detection to improve data protection of enterprises. The suggested framework is the systematic analysis of the activity logs of the database, detection of sensitive data access patterns, and the assessment of the levels of risk associated with queries with intelligent detection models. A behavior analysis module obtains the regular patterns of user interaction and identifies the unusual behavior, which can suggest the appearance of some suspicious or unauthorized actions. The framework was tested and assessed with database activity logs on the enterprise level provided on the Oracle and PostgreSQL database environments and were based on millions of query records reflecting the actual operational loads. In experimental analysis, it has been shown that the suggested monitoring framework is much better in detecting anomalies than legacy rule-based monitoring systems. The system reached about 92% detection, 88% anomaly recall and 90% precision in detecting suspicious database queries as well as abnormal access pattern. The framework effectively identified sensitive data access attempts and potential data leakage scenarios while maintaining low false-positive rates during high-volume database operations. The results indicate that integrating artificial intelligence with behavioral analytics substantially strengthens enterprise database monitoring capabilities and provides a scalable solution for protecting sensitive organizational data in complex database infrastructures.