Abstract Managing legal change is a growing challenge for compliance departments due to the increasing complexity and volume of regulations. Failure to adapt business processes to legal updates can result in severe consequences such as fines or reputational damage. Despite the importance of early legal change analysis, research on legal knowledge change management and business process compliance remains fragmented. This work addresses this gap through a systematic literature review covering outlets in Artificial Intelligence, Law, Business Process Management, Natural Language Processing, and Requirements Engineering. The authors identify four research streams and four key activities from change representation to impact analysis, and highlight a lack of integration between legal changes and process compliance. As a first step towards this integration, LegalChanges4BPC is proposed, an automated approach that detects legal changes and analyzes their relevance for business process compliance. To evaluate its feasibility, the authors apply prompt-based techniques with Large Language Models (LLMs) across two regulatory datasets. GPT-5 and Mistral-3.1 show the best balance of completeness and correctness, while Phi-4 and LLaMA-4 excel in efficiency, revealing trade-offs between models across the evaluated aspects. The automated approach employs prompting strategies for legal change analysis and contributes toward automated compliance, legal traceability, and contextual reasoning with LLMs.
Barrientos et al. (Mon,) studied this question.