Data Provenance Standards explained for data suppliers

What are the Data Provenance Standards?#

The Data Provenance Standards are a metadata specification published by the Data & Trust Alliance, a not-for-profit consortium that the specification says was established in September 2020. Version 1.0.0 groups dataset metadata into Source, Provenance and Use, and states that this metadata is needed 'to enable proper dataset selection for AI Model Training.'

The standards are released under the Creative Commons CC-BY-SA-4.0 license, with code and supporting artifacts under Apache 2.0, so a supplier can use the structure without asking permission. They are voluntary: no law requires them, but they give buyers and suppliers a shared vocabulary for questions every licensing deal asks anyway.

For a company licensing operating records, the value is practical. A buyer's diligence questionnaire and a supplier's documentation can follow the same field list instead of being reconciled line by line over email.

What does each group ask a supplier to document?#

Each group in the Data Provenance Standards asks a supplier a different question. Source identifies the dataset and who stands behind it, Provenance describes where the data came from and how it came to exist, and Use sets out the rights, privacy treatment and permitted purposes.

The GitHub version of the specification lists six elements under Source, nine under Provenance and twelve under Use. The table summarizes the question each group answers, where a supplier usually finds the answers, and which part of the SourceX Evidence Packet holds the same information.

Release authorization, the fifth part of the Evidence Packet, has no direct counterpart among the three groups. It records who approved the release and under which contract, which buyers usually request separately.

The Use group, element by element#

The Use group is the part a buyer's counsel reads first, because it carries rights and privacy information. Its elements include confidentiality classification, consent documentation location, privacy-enhancing technologies applied, data processing geography included or excluded, data storage geography allowed or forbidden, license to use, intended data use, and copyright, patent and trademark status.

The specification's code list for privacy-enhancing tools includes redaction, masking, minimization, pseudonymization, tokenization, encryption, k-anonymity, l-diversity, t-closeness and differential privacy, among others. Using those terms in your own preparation logs means the Use group largely fills itself in.

Why buyers now ask for provenance metadata#

Buyers now ask for provenance metadata because they must explain their own training data choices to customers, auditors and regulators. California's AB 2013, signed on September 28, 2024, requires developers of generative AI systems to post training-data documentation that states, among other things, whether datasets were purchased or licensed and whether they include personal information. In the EU, Article 53(1)(d) of the AI Act requires general-purpose model providers to publish a summary of training content using an AI Office template, which the European Commission released on July 24, 2025.

Those duties sit with the AI developer, not the supplier, but they flow back through diligence. A buyer that has to say whether data was licensed, and whether it held personal information, will ask its suppliers for the facts in a form it can file.

The research community moved the same way. The Data Provenance Initiative audited 44 data collections spanning more than 1,800 fine-tuning text datasets, documenting their sources, licenses and creators, and its tools generate a Data Provenance Card for any filtered subset. Earlier work such as Datasheets for Datasets, published in Communications of the ACM in December 2021, and MLCommons' Croissant format with its Responsible AI extension set similar expectations.

For a supplier, the lesson is simple. Provenance answers prepared in a recognized structure are easier for a buyer to review, while ad hoc answers invite rounds of follow-up questions.

How to produce provenance metadata for a licensed dataset#

Producing provenance metadata for a licensed dataset is mostly a matter of collecting answers the licensing process already generates, then recording them per dataset version.

Keep the metadata with the dataset card and the file manifest. Together they let a buyer confirm that the records delivered match the records described.

• 1Start from the system inventory: each source system, its owner, date range and export method.
• 2Record the collection context: which notices, terms or consents applied when the records were created.
• 3Log every preparation step with the method, tool and reviewer, using the standard privacy-enhancing tool terms.
• 4Attach the rights review: contracts checked, carve-outs made and who owns any protected material.
• 5Fill the Use elements from the signed license: permitted purposes, geography limits and confidentiality class.
• 6Version the metadata with the dataset, so a refreshed delivery gets a new record instead of an edited old one.

Illustrative: an engineering firm answers a provenance request#

Illustrative: a fictional mechanical and electrical engineering firm is preparing project records, RFIs, submittal reviews and internal design review comments from Deltek and Procore for license. The buyer's diligence team asks for metadata aligned with the Data Provenance Standards.

The firm's operations lead fills Source and Provenance from the data inventory: systems, project years, export methods and preparation lineage. For Use, the general counsel records the confidentiality class, where employee notices and client contract reviews are kept, the privacy-enhancing tools applied, storage limited to the United States, the license itself, and training and evaluation as the permitted purposes.

The copyright element changed the scope. Drawings and specifications delivered to clients were marked as client-controlled and excluded, while the firm's internal review comments and RFI histories stayed in. The metadata shipped with the dataset card and the manifest.

How SourceX maps provenance to the Evidence Packet#

SourceX maps provenance to the SourceX Evidence Packet, which records provenance, licensing rights, permitted use, the privacy record and release authorization for each dataset. Those sections cover the questions the Source, Provenance and Use groups ask, and release authorization adds the supplier's sign-off.

The inputs come from the SourceX five-step transaction: Supply documents systems and history, Rights documents contracts and carve-outs, Preparation documents privacy methods, and Approval and Delivery document what was released, to whom and when.