Business process compliance ensures that organizational processes conform to external regulations (e.g., SOX, GDPR, Basel accords) and internal policies. The importance of compliance has grown steadily, driven by an ever-increasing number of regulations, the complexity of global business operations, and the severe financial and reputational consequences of non-compliance. Despite significant research advances in modeling compliance rules, verifying processes at design-time, and monitoring their execution at run-time, current business process compliance approaches still face major limitations.

Existing frameworks are often confined to specific compliance checking strategies (such as design-time, run-time, or auditing) and offer only limited support for scalability, usability, and reasoning across multiple regulatory domains. Moreover, research shows that key functionalities such as predictive monitoring of violations and root-cause analysis remain largely underexplored, leaving organizations with reactive, after-the-fact mechanisms. However, compliance management must be dynamic and continuous, as regulations continually evolve, whether due to legal changes, shifts in policies, or new business practices. In this direction, the research community highlights the need for more proactive, intelligent systems that can quickly adjust to regulatory changes and offer clearer, actionable insights.

Artificial Intelligence (AI) offers new opportunities to address these challenges. Advances in natural language processing, predictive analytics, explainable AI (XAI), and human-in-the-loop methods open avenues to reimagine compliance management. Rather than focusing solely on static rule-checking, AI-powered compliance solutions could continuously analyze streams of data, anticipate violations before they occur, and provide actionable, human-understandable explanations to compliance officers. This aligns with the emerging vision of AI-Augmented Business Process Management Systems (ABPMSs), where humans and AI collaborate to ensure trustworthy and adaptive compliance management.

The goal of this Dagstuhl Seminar is to bring together experts from Business Process Management (BPM), Compliance Management, AI and Law, AI Ethics, and XAI to explore the foundations, challenges, and opportunities for leveraging AI in business process compliance. In this direction, the seminar will be structured around four interrelated themes, each addressing a core research and practice challenge for AI-enabled business process compliance, namely:

1. How to extract and formalize compliance rules using AI to enable explanation and reasoning about compliance violations?
2. How to integrate AI with human expertise in compliance management?
3. What are the organizational, legal, ethical, and societal implications of AI-enabled compliance?
4. How to enable predictive and adaptive compliance monitoring?

By fostering interdisciplinary discussions, the seminar aims to chart a roadmap for next-generation compliance solutions that are explainable, predictive, and human-centered, bridging the gap between technological innovation, regulatory governance, and real organizational practice.

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Cristina Cabanillas, Andrea Marrella, Monica Palmirani, and Karolin Winter

This seminar qualifies for Dagstuhl's LZI Junior Researchers program. Schloss Dagstuhl wishes to enable the participation of junior scientists with a specialisation fitting for this Dagstuhl Seminar, even if they are not on the radar of the organizers. Applications by outstanding junior scientists are possible until August 28, 2026.