From policy to action: a cross-regulatory conceptual framework to address data and AI regulations
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Date
2026-05-17
Authors
Yu, Fangzhou
Carton, Fergal
Xiong, Huanhuan
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor and Francis Ltd.
Published Version
Abstract
Organisations operating in data- and AI-intensive environments face an increasingly fragmented and overlapping EU regulatory landscape, including the GDPR, AI Act, etc. Existing research largely addresses these regimes in isolation, offering limited guidance on integrated organisational compliance. This study develops a cross-regulatory integration framework that translates multi-regulatory obligations into structured organisational capabilities, explicitly assigning clear actors and responsibilities. Drawing on Actor-Network Theory (ANT) and a Strategic-Tactical-Operational (STO) decision-layer model, this study conceptualises compliance as a networked and multi-level governance process. The framework supports organisations in aligning ethical criteria, technical controls, and governance responsibilities across regulatory domains, advancing enterprise-wide digital governance and compliance management through both operational and functional processes.
Description
Keywords
Actor-Network Theory , AI ACT , Cross-regulatory compliance , GDPR , [CUBS]
Citation
Yu, F, Carton, F & Xiong, H 2026, 'From policy to action: a cross-regulatory conceptual framework to address data and AI regulations', Journal of Decision Systems, vol. 35, no. 1, 2669329, pp. 1-10. https://doi.org/10.1080/12460125.2026.2669329