AI-Driven Database Security: Proactive Detection, Response to SQL Injections, and Real-Time Anomaly Detection & Threat Mitigation with Machine Learning

Santosh Jaini

doi:10.36676/jrps.v12.i3.1599

Authors

Santosh Jaini Independent Researcher

DOI:

https://doi.org/10.36676/jrps.v12.i3.1599

Keywords:

AI-Driven Database Security, Proactive Detection

Abstract

The Enterprise databases are managed by ML & AI, providing real-time threat detection monitoring & resource management, and Anomaly detection. This paper examines the use of machine learning algorithms on database predictive capacities for SQL injection detection, anomaly response, and threat diminution. An article documenting a simulation scenario shows how AI can leverage the efficiency of database resources in as much as shortcomings, including integration issues and false positives, may be present are dealt with. Part and parcel of this report are practical cases and scenarios demonstrating AI in database security and resource management. Potential solutions concerning major issues under study are discussed, and further field development is outlined.

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