AI-driven compliance monitoring frameworks for automated detection and classification of data privacy violations in hybrid infrastructures
Year: 2025
Type: article
Abstract: This article examines the use of Artificial Intelligence in compliance monitoring with a specific focus on the detection and classification of data privacy breaches in hybrid environments. Organizations now grapple with unparalleled regulatory risks arising from the integration of cloud and on-premises computing. Existing compliance approaches appear to fall short in grappling with the challenges posed by dispersed and ever-evolving environments. This research proposes a conceptual AI-based framework for compliance monitoring and its impact on data privacy management. Results show that AI components, especially in machine learning, natural language processing, and predictive analytics, improve accuracy, diminish manual errors, and facilitate the instantaneous reaction to breaches. A designed framework monitoring a simulated dataset demonstrates the ability to classify and identify breaches with quantifiable efficiency.
Author: Olomina, Jennifer
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7087127477
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