Compliance Generation for Privacy Documents under GDPR: A Roadmap for Implementing Automation and Machine Learning
Abstract
Most prominent research today addresses compliance with data protection laws through consumer-centric and public-regulatory approaches. We shift this perspective with the Privatech project to focus on corporations and law firms as agents of compliance. To comply with data protection laws, data processors must implement accountability measures to assess and document compliance in relation to both privacy documents and privacy practices. In this paper, we survey, on the one hand, current research on GDPR automation, and on the other hand, the operational challenges corporations face to comply with GDPR, and that may benefit from new forms of automation. We attempt to bridge the gap. We provide a roadmap for compliance assessment and generation by identifying compliance issues, breaking them down into tasks that can be addressed through machine learning and automation, and providing notes about related developments in the Privatech project.
Keywords
Cite
@article{arxiv.2012.12718,
title = {Compliance Generation for Privacy Documents under GDPR: A Roadmap for Implementing Automation and Machine Learning},
author = {David Restrepo Amariles and Aurore Clément Troussel and Rajaa El Hamdani},
journal= {arXiv preprint arXiv:2012.12718},
year = {2020}
}
Comments
14 pages, The paper was presented at GDPR Compliance Theories, Techniques, Tools a Workshop of JURIX 2019. Universidad Polit\'ecnica de Madrid, Madrid, Spain, 11 December 2019