The exchange of data across systems, both within and between organizations, is governed by company policies and data protection regulations. As policies and data flows evolve over time, ensuring continuous compliance of data exchange remains a complex challenge. In federated data architectures, the validation of data access requests is a critical and labor-intensive process. Previously, constraints were formalized into data contracts using rule-based specification languages for automated compliance and access verification. However, these constraints originate from legal documents, and translating them into formal data contracts is tedious and error-prone. Each update to source documents required repeating the translation process, increasing the risk of inconsistencies. To address this challenge, we developed Governance AI, a large language model-based tool for evaluating data access requests based on relevant policies, the type of data requested, and the request's context. This approach streamlines governance processes while maintaining compliance with evolving regulatory requirements.

Recently several approaches to federated data governance and decentralized data operations have been proposed. Data mesh, one of the most prominent of these approaches, specifically targets how to automate and scale governance at large organizations. Governance tasks, such as ensuring data protection compliance, are notoriously challenging to automate. This paper proposes new ways to verify data declassification via contract-based methods. The design benefits from compositional characteristics of data mesh and builds upon existing contract-testing solutions from the microservices domain.

Data mesh is a socio-technical approach to decentralized analytics data management. To manage this decentralization efficiently, data mesh relies on automation provided by a self-service data infrastructure platform. A key aspect of this platform is to enable decentralized data governance. Because data mesh is a young approach, there is a lack of coherence in how data mesh concepts are interpreted in the industry, and almost no work on how a data mesh platform facilitates governance. This paper presents a conceptual model of key data mesh concepts and discusses different approaches to drive governance through platform means. The insights presented are drawn from concrete experiences of implementing a fully-functional data mesh platform that can be used as a reference on how to approach data mesh platform development.