Documenting the origin of training data is moving from a good practice to a formal expectation, and in the EU it is now written into law. Since 2 August 2025, providers of general-purpose AI models placed on the EU market have been required to publish a public summary of their training content using a template published by the European Commission, and models already on the market before that date have until 2 August 2027.
This blog shares what we see teams doing in practice around consent and provenance: the difference between a lawful basis for personal data and a rights basis for copyrighted or licensed content, the kind of documentation many teams keep per source before a model ships, and how organizations approach building a training corpus that holds up when a regulator, an enterprise buyer, or their own legal team starts asking questions.
Key Takeaways
- Documentation is now an enforceable requirement, not a best practice. The EU AI Act’s training-data summary obligation for general-purpose models is live law with an active enforcement date, and it sits alongside a parallel data-governance requirement for high-risk systems.
- Consent and licensing are not the same question. Personal data needs a lawful basis to process, typically consent or a defined legitimate interest. Copyrighted or licensed works need a rights basis: a license, a purchase, or a public domain or open-license status. Treating the two as one checkbox misses half the exposure.
- A provenance record is a per-source obligation, not a corpus-level summary. Acquisition method, license or consent basis, date, and chain of custody need to exist for each source category, because that is the granularity any regulatory filing, procurement review, or internal audit actually asks for.
- Documentation built after the fact is worth less than documentation built during sourcing. Recreating provenance retroactively from a corpus that already exists is expensive, incomplete, and unconvincing to a skeptical reviewer in a way that contemporaneous records are not.
- Clear, provable sourcing is becoming a practical baseline. As documentation expectations spread from regulation into ordinary procurement, sourcing data with a well-documented basis is increasingly the simpler path.
Why Documentation Became a Live Requirement
The EU AI Act’s Article 53 obligation is worth understanding in some detail, because its structure offers a useful preview of where AI documentation expectations are heading. Providers of general-purpose AI models are required to publish a summary of their training content, using a standardized template the European Commission published. The template covers data modalities, source categories such as public datasets, licensed private data, and web-scraped content, and how the provider addresses copyright and data protection obligations.
What makes this structurally important, beyond any one requirement, is what it establishes as normal. A summary of training content, organized by source category, with a rights and consent basis attached, is now a standard artifact that a regulator can request and a template that already defines what belongs in it. High-risk systems face a parallel, more detailed obligation under Article 10: documented governance of training, validation, and testing data, including examination for possible biases. Two different obligations, aimed at two different categories of AI systems, converging on the same underlying ask: show your work on where the data came from and what gives you the right to use it.
Enterprise buyers have started asking the same question independent of any regulatory requirement, because their own exposure depends on their vendors’ answers. A procurement review that used to stop at a security questionnaire increasingly includes a data provenance questionnaire, and a vendor without a good answer is a vendor whose risk is now the buyer’s risk too. The trend is the same whether the pressure comes from a regulator, a customer, or your own board: documentation moved from a differentiator to a baseline expectation.
How Teams Approach Consent for Training Data
Personal Data Needs a Lawful Basis, Not Just a License
When training or fine-tuning data includes personal information (names, behavior, images of identifiable people, customer interactions), licensing content does not resolve the exposure on its own. Frameworks such as the GDPR (the EU’s General Data Protection Regulation) require a lawful basis for processing personal data, and consent is only one of several available bases, each with different conditions.
Consent for AI training generally needs to be informed about that use. A person who agreed to a privacy policy written before generative AI existed may not have consented to having their data used to train a model, and privacy regulators have increasingly raised questions about whether older consents extend to AI training. In practice, the teams we work with aim for a documented basis, tied to the specific use, for every source that contains personal data.
Copyrighted and Licensed Works Need Provenance You Can Prove
For creative, published, or proprietary content, the operative question is rights, not privacy. A work is usable when it is owned, licensed on terms that cover AI training, or genuinely in the public domain or under a license that permits the use. The rights question and the privacy question are independent: a dataset can clear one and fail the other, which is why a provenance program needs both checks running for every source, not a single approval step that assumes one covers the other.
The Provenance Record That Survives Scrutiny
A provenance record that would hold up under scrutiny answers the same handful of questions for every source category in a training corpus, not for the corpus as a whole. What is the source, and how was it acquired: purchased, licensed, scraped under what terms, collected directly with what consent flow, or received from a vendor. What is the legal basis: a specific license with its terms, a documented consent basis and its scope, a public domain or open license designation with its citation, or a legitimate-interest assessment for personal data. When was it acquired, and under what version of the source’s terms, since terms change and the version in effect at acquisition is what governs. Who is accountable for the record, so that when a question arrives it has an owner rather than a search. And what is the chain of custody from acquisition through any transformation to its use in a specific model version, since a model trained on a corpus that later gets partially remediated needs to know which version it actually learned from.
Building a Training Corpus That Survives Scrutiny
The organizations getting this right treat provenance as a sourcing-time discipline rather than a filing-time reconstruction project. Sourcing categories get defined and approved in advance: owned content, licensed content with AI-training terms confirmed, public domain and open-licensed content with citation, and directly collected content with a consent flow built for the actual use. Every acquisition decision then maps to a pre-approved category instead of a judgment call made under deadline pressure.
Anything outside those categories escalates before it enters the corpus, not after. Sampling audits check that the documentation for a random slice of the corpus actually exists and actually supports the claimed category, on a cadence, rather than assuming the intake process worked. And version-level custody tracks which corpus version trained which model, so that a provenance question discovered later can be scoped to what it actually affects instead of calling every model into question at once.
How Digital Divide Data Can Help
Whether a program builds this discipline internally or with a partner, the same three things decide whether the corpus survives scrutiny: sourcing that maps to pre-approved categories, documentation built at acquisition time rather than reconstructed later, and a consent or rights basis that’s specific to AI training use. Producing those is the work we do.
Sourcing you can document. Data collection and curation runs against defined sourcing categories with the license, consent, or ownership basis captured at the point of acquisition, not assembled afterward under a deadline.
Provenance that holds up per source, not per corpus. AI data preparation maintains the acquisition method, basis, date, and chain of custody at the granularity a regulatory filing or procurement review actually asks for.
Delivery that tracks corpus versions to model versions. Data engineering for AI keeps the version-level custody chain intact, so a provenance question about one dataset doesn’t become a question about every model that was ever near it.
If your next question is which sources in your current training data you could document on 24 hours’ notice, that’s the assessment we run. Talk to an expert.
Conclusion
The trend we see across AI regulation and across the procurement conversations enterprise buyers are starting to have is consistent: the question is no longer just whether a model performs well, but whether the organization that built it can show, source by source, where the data came from and what gave them the right to use it.
That is a workable standard to build a data program around: documented sourcing, a clear consent or rights basis per source, and records built while the data was acquired rather than reconstructed once someone asks. The test is simple to state and hard to fake.
Can you produce, for any source in your training corpus, what it is, how it was acquired, and what gave you the right to use it? If that answer takes weeks to assemble, the gap is usually not the paperwork. It is that the discipline was never built into sourcing in the first place.
References
European Union. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act), Articles 10 and 53. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689
European Commission. (2025). Explanatory notice and template for the public summary of training content for general-purpose AI models. https://digital-strategy.ec.europa.eu/en/library/explanatory-notice-and-template-public-summary-training-content-general-purpose-ai-models
European Commission. (2025, July 24). Commission presents template for general-purpose AI model providers to summarise the data used to train their model [Press release]. https://digital-strategy.ec.europa.eu/en/news/commission-presents-template-general-purpose-ai-model-providers-summarise-data-used-train-their
Frequently Asked Questions
Q1. We only operate in the US. Does the EU’s training-data summary requirement affect us?
Possibly, and the determination depends on where your models are placed on the market or put into service, which is a question for counsel rather than a blog post. What’s worth knowing going into that conversation: the obligation attaches to making a general-purpose model available in the EU, not to where the company is headquartered, so a US-based provider serving EU customers or users can fall in scope regardless of domestic law. Even for organizations genuinely outside its reach, the template the Commission published is becoming a reference format that enterprise buyers and other regulators borrow from, which means the documentation habit it requires is worth building regardless of where the legal obligation formally applies.
Q2. How is this different from the data privacy compliance work we already do?
Overlapping but not identical, and the gap is often where AI training exposure tends to hide. Existing privacy compliance typically covers processing personal data for the purposes stated when it was collected, such as customer service, transactions, and account management. AI training is often a new purpose that was not contemplated in the original consent or legitimate-interest basis, which means an existing compliance program may be sound for its original purpose and still leave the training use uncovered. A practical step many teams take is a purpose-specific review with their legal team: for any personal data entering a training set, confirm the basis covers this use, not just some use.
Q3. What about content we scraped from public websites years ago, before any of this regulation existed?
Age does not resolve the exposure, and in some ways it can compound it, since older scraping was often done with less documentation of what was taken and which site terms applied at the time. A practical path many teams follow is retrospective triage rather than a blanket assumption either way: inventory what was scraped and when, check with counsel whether the source’s terms at that time restricted the use, and separate sources with a clear basis from sources without one. Content that cannot clear that review is usually prioritized for remediation or removal, with the models trained on it tracked so the exposure is scoped rather than diffuse.
Q4. Our training data includes third-party vendor datasets. Whose responsibility is the provenance record?
Contractually the vendor’s, practically yours as well, because your model’s exposure doesn’t stop at the contract boundary. The workable structure is flow-down: contractual warranties from the vendor about how their data was acquired and what rights they’re conveying, backed by an actual right to audit rather than a warranty alone. A vendor’s promise is only as good as your ability to verify it. In our experience, vendor data that arrives without documentation, or with documentation the vendor will not let you inspect, is safer to treat as undocumented for your own risk purposes, regardless of what the contract says, because in a review you are likely to be the one asked to produce the record.
Q5. How far back do we need to go to document data we’ve already trained models on?
Many teams prioritize by exposure rather than chronology. They start with sources feeding models currently in production or customer-facing, since that is where a provenance gap has the most immediate consequence, then work backward through models still being actively developed. Deprecated models trained on undocumented data usually carry lower urgency, since the practical risk is tied to continued use. For any source where documentation cannot be reconstructed, common options include remediation, replacing that source with a documented alternative, or an explicit, recorded risk acceptance agreed with counsel, rather than leaving the gap silent. A gap that has been identified and assessed is a very different position from a gap nobody has looked for yet.
Disclaimer
This article is provided for general informational purposes only and does not constitute legal advice. Regulatory requirements, timelines, and their application to your organization depend on your specific circumstances and may change over time.
