Automated compliance monitoring: A machine learning approach for digital services act adherence in multi-product platforms

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 errorprone. 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 multiplatform 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.