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Automated Compliance Monitoring: A Machine Learning Approach for Digital Services Act Adherence in Multi-Product Platforms
- 1 Computer Science, University of Southern California, Los Angeles, CA
- 2 Computer Science, University of Southern California, Los Angeles, CA, USA
- 3 Computer Science, University of Texas at Dallas, Richardson, USA
- 4 Business Intelligence, Engineering School of Information and Digital Technologies, Villejuif, France
* Author to whom correspondence should be addressed.
Abstract
This paper presents an innovative machine learning approach for automated compliance monitoring of Digital Services Act (DSA) requirements across multi-product digital platforms. The proposed framework addresses the significant challenges of monitoring regulatory compliance in complex digital environments where manual verification processes prove insufficient and error-prone. The methodology introduces a formalized representation of DSA requirements through algorithmic processing and transforms these into machine-verifiable specifications using metamorphic testing principles and timed automata models. The core architecture implements a hybrid risk assessment model combining supervised and unsupervised learning techniques to evaluate compliance across heterogeneous platform environments. Comprehensive evaluation across multiple digital service categories demonstrates detection accuracy between 0.86-0.94 (F1-score) with processing efficiency ranging from 78% to 95% depending on platform characteristics. The multi-platform data integration pipeline achieves near real-time monitoring capabilities while respecting data protection constraints. The framework addresses key technical challenges including the complexity of requirement formalization, data access limitations, and adaptation to evolving regulatory interpretations. This research contributes significant advancements toward automated, scalable compliance verification solutions essential for effective implementation of the Digital Services Act across diverse digital service ecosystems.
Keywords
Digital Services Act, Compliance Monitoring, Machine Learning, Multi-platform Verification
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Cite this article
Wang,H.;Wu,J.;Ni,C.;Qian,K. (2025). Automated Compliance Monitoring: A Machine Learning Approach for Digital Services Act Adherence in Multi-Product Platforms. Applied and Computational Engineering,147,14-25.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Mechatronics and Smart Systems
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