Abstract :

In This Paper, The Authors Discuss The Execution Of Self-learning Security Controls In A Dynamic Regulatory Environment. It Emphasizes The Fact That The Adaptive Security Systems Can Be Dynamically Adapted To The Changing Threats And Regulatory Demands With The Help Of Machine Learning (ML) And Deep Learning (DL) Models, Including GDPR, HIPAA, And CCPA. The Paper Refers To Reinforcement Learning, Convolutional Neural Networks, And Long Short-term Memory Networks To Help In Boosting Cybersecurity Through Constant Network Behavior Observation, Anomalies Detection, And Compliance. The Effectiveness Of These Models In Real-time Threat Detection, Manual Minimalization, And Enhancement Of Risk Reduction, And Regulatory Consistency Is Evidenced By Simulations And Real-life Examples.

Published:

18-4-2025

Issue:

Vol. 25 No. 4 (2025)

Page Nos:

425-431

Section:

Articles

License:

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

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