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

Author

Listed:
  • Mr. Jalindhar Banshi Kachule

    (Department of Computer Science and Engineering Branch: Information Security)

  • Prof. Badrinath Bulepatil

    (Department of Computer Application Branch: Information Technology)

  • Prof. Vishal Gejge

    (Department of Computer Application Branch: MCA)

  • Prof. Atish Ashokrao Shriniwar

    (Department of Computer Application Branch: MCA)

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.

Suggested Citation

  • Mr. Jalindhar Banshi Kachule & Prof. Badrinath Bulepatil & Prof. Vishal Gejge & Prof. Atish Ashokrao Shriniwar, 2025. "AI for Database Security & Anomaly Detection: Leveraging Machine Learning for Real-Time Threat Identification," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 14(8), pages 1039-1045, August.
    • Handle: RePEc:bjf:ijltem:v:14:y:2025:i:8:a:1709
    DOI: 10.51583/IJLTEMAS.2025.1408000133
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