Provenance, Traceability & Observability
In some cases, apart from the data itself, participants also need to know where it came from, how it has been used, by whom, and how certain decisions or outcomes can be understood. According to the DSSC, the provenance, traceability & observability building block enables them to provide the evidence trail that supports trust, compliance, and accountability.
Provenance captures the origin and history of data (who created it, how it was processed).
Traceability records what happened during a transaction (who accessed it, under what terms, and what actions were performed).
Observability helps make decisions, events, or outcomes understandable for monitoring, troubleshooting, and operational control.
In practice, DSSC 3.0 distinguishes between evidence generated by participant agents and, where needed, the use of a dedicated observability service to collect and store relevant logs centrally. Which events need to be recorded, how logs are structured, who may access them, and whether storage is local or centralised should be defined in the data space rulebook.
See the complete DSSC description here.
These mechanisms are especially important in regulated sectors or when dealing with high-value data, including AI-related use cases, where knowing the origin, transformation, and permitted use od data helps support regulatory compliance, responsible reuse, and overall reliability of AI-driven services within the ecosystem (e.g., in finance, healthcare, logistics, or AI, where the EU AI Act may apply).
They also play a crucial role in meeting obligations set out in European legislation, including the EU Data Act and the Data Governance Act (DGA), which emphasise transparency, interoperability, and accountability in data sharing.
iSHARE aligns with these European frameworks, ensuring that its trust, governance, and technical specifications reinforce the same goals: legal clarity, accountability, and cross-sector interoperability in data exchanges.
Because provenance, traceability & observability data are valuable in themselves, they must be handled with the same care as the primary data. This includes:
Clear semantics and structure so it can be understood.
Access and usage rules so only authorised parties can see or use it.
A balanced approach to granularity: detailed enough to build trust and meet compliance needs, but not so heavy that it slows down the system.
The iSHARE Trust Framework does not prescribe a single approach here. Data spaces are free to design solutions that fit their governance and legal context, whether centralised, decentralised, or distributed. What matters is that the chosen model supports scalability, security, and alignment with contractual and regulatory requirements.
Provenance, Traceability & Observability connects closely with other building blocks
Data Models: Ensures provenance and traceability data is understandable across participants.
Data Exchange: Shares provenance data securely using standard protocols.
Identity and Attestation Management & Access and Usage Policies Enforcement: Controls who can access provenance data.
Data Value Creation Enablers: Uses provenance data to create added value
Business: Supports agreements and governance based on provenance data.
Legal: Ensures provenance data meets regulatory and contractual requirements.
Governance: Defines rules and maintenance for provenance and traceability across the data space.
The guiding questions can help in the co-creation process and in defining this building block, so please see the next section.
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