AI Governance, Data Regulation, and Digital Compliance

Permanent URI for this collectionhttps://hdl.handle.net/10125/112531

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    Agentic AI for Financial Crime Compliance
    (2026-01-06) Axelsen, Henrik; Licht, Valdemar; Damsgaard, Jan
    The cost and complexity of financial crime compliance (FCC) continue to rise, often without measurable improvements in effectiveness. While AI offers potential, most solutions remain opaque and poorly aligned with regulatory expectations. This paper presents the design and deployment of an agentic AI system for FCC in digitally native financial platforms. Developed through an Action Design Research (ADR) process with a fintech firm and regulatory stakeholders, the system automates onboarding, monitoring, investigation, and reporting, emphasizing explainability, traceability, and compliance-by-design. Using artifact-centric modeling, it assigns clearly bounded roles to autonomous agents and enables task-specific model routing and audit logging. The contribution includes a reference architecture, a real-world prototype, and insights into how Agentic AI can reconfigure FCC workflows under regulatory constraints. Our findings extend IS literature on AI-enabled compliance by demonstrating how automation, when embedded within accountable governance structures, can support transparency and institutional trust in high-stakes, regulated environments.
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    An Empirical Analysis of Design Principles and Functional Requirements for Data Trustees
    (2026-01-06) Steinert, Michael; Tebernum, Daniel
    Organizations increasingly rely on sharing data to drive collaboration and gain a competitive advantage. However, privacy, security, and regulatory compliance issues often prevent the sharing of sensitive information. Data trustees have emerged as a potential solution to these challenges, but there is still significant ambiguity about the key elements and best practices that define them and the related technologies. This lack of clarity hinders the development and adoption of data trustees, stalling progress in the field. In response, we conducted a comprehensive survey of the data trustee community and received 35 complete responses from experts in various data governance roles. Our findings reveal common trends, important needs, and expectations for data trustees. By synthesizing these insights, our study contributes to the practical and theoretical understanding of the design principles and functional requirements for data trustees. It provides guidance for industry practitioners and extends the evidence-based discourse in the academic community.
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    Who Matters in AI Compliance? A Stakeholder Framework for Enterprise Strategies
    (2026-01-06) Kost, Leonard; Webster, Samantha; Breitner, Michael H.
    As artificial intelligence (AI) becomes central to enterprise operations, aligning AI strategies with regulatory developments is increasingly vital. This study proposes a stakeholder-involved maturity model to embed AI compliance into organizational processes. Design Science Research-oriented, the maturity model integrates findings from literature, expert interviews, a focus group discussion, and an evaluative single case study. Including a four-staged maturity assessment, 12 relevant stakeholders with specific responsibilities are identified to address AI regulation in enterprise AI strategies. Results show varying stakeholder relevance across maturity levels and highlight the importance of cross-functional collaboration across crucial department units. The model offers a triangulation of insights in a structured management framework. Based on a comprehensive stakeholder analysis and a consequential involvement intensity for AI regulation integration in enterprises, we develop and contribute to research by providing a structured and actionable framework for organizations aiming to implement and transition to increased AI compliance.
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    Introduction to the Minitrack on AI Governance, Data Regulation, and Digital Compliance
    (2026-01-06) Lindgren, Rikard; Saadatmand, Fatemeh