ML TO MEASURE EFFECTIVENESS OF DATABASE SECURITY

Authors

DOI:

https://doi.org/10.70715/jitcai.2026.v3.i2.049

Keywords:

Artificial Intelligence, Machine Learning

Abstract

Enforcing database security to protect data is crucial in today’s digital world. As modern organizations increasingly rely on cloud infrastructure to run their business operations, database security becomes essential to protect the data of critical assets against malicious actors, while also maintaining compliance and regulatory requirements. This paper explores a machine learning approach to measuring the effectiveness of Database security. It utilizes logs from the AWS RDS service to construct a representative dataset, with key features including query latency ms, user role, access hour, query type, security alert, and top threat type. These features are used to train a model to predict the effectiveness of database security using AWS RDS log patterns. This enables organizations to prevent data breaches, protect sensitive information, and ensure ongoing compliance with relevant regulations.

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References

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Published

03/30/2026

Issue

Vol. 3 No. 2 (2026): Advances, Innovations, and Emerging Research across the interconnected domains of Information Technology, Cybersecurity, and Artificial Intelligence

Section

Articles

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Copyright (c) 2026 Upakar Bhatta (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
U. Bhatta, “ML TO MEASURE EFFECTIVENESS OF DATABASE SECURITY”, Journal of IT, Cybersecurity, & AI, vol. 3, no. 2, pp. 1–12, Mar. 2026, doi: 10.70715/jitcai.2026.v3.i2.049.

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