Approving third-party training data: a guide for AI governance leads

This page is general information, not legal advice. Confirm requirements with counsel for your jurisdiction and use case.

Why an intake gate beats after-the-fact review

An intake gate works because a dataset is cheap to reject before training and close to impossible to remove afterward. Once a corpus has shaped weights, an unlicensed or poorly consented source can mean retraining, a takedown fight or a disclosure you cannot back up. The usual failures are well known. A research team copies a vendor sample into a shared bucket. A RAG index pulls in a partner's documents under an NDA that never allowed machine learning use. An evaluation set leaks into fine-tuning data.

The governance lead does not need to judge every column. The job is to make sure the right reviewers saw the right evidence and that the decision is recorded. Legal clause review belongs with counsel (see reviewing an AI data license as in-house counsel). Turning license terms into storage and pipeline controls belongs with ML data engineering teams. The other buyer roles are covered in the data sourcing by buyer team hub.

The five evidence items every approval needs

Approval should depend on five pieces of evidence, each with a named reviewer and a pass condition. The table below is a starting gate. Adjust the reviewers to fit your organization.

Illustrative example: invented to show structure; it does not describe an available dataset.

Two conditions should block approval outright. One is a license that is silent on model training or on use of outputs. The other is a supplier that cannot describe how the data was collected. Our data provider due diligence questionnaire turns these items into supplier questions, and the supplier security review covers transfer and storage.

Mapping the gate to ISO/IEC 42001 and the NIST AI RMF

One gate can produce evidence for both frameworks, because both expect documented processes for data acquired from outside the organization. ISO/IEC 42001 sets requirements for an AI management system (AIMS) for organizations that develop, provide or use AI [1]. Secondary summaries say its Annex A has 38 controls under nine objectives. Data controls sit under A.7 (data for AI systems: acquisition, quality, provenance and preparation), and supplier relationships sit under A.10 [2]. The standard is paywalled, so check exact control numbers and wording against your licensed copy before you write them into policy.

The NIST AI RMF 1.0 is voluntary and organized around four functions: GOVERN, MAP, MEASURE and MANAGE [3]. Third-party data appears in GOVERN, which expects policies for risks from third-party entities, including intellectual property infringement. It also appears in MAP, which expects organizations to map legal and technology risks of components such as third-party data [3]. The Generative AI Profile, NIST AI 600-1, names intellectual property and data privacy among its 12 generative AI risks. That supports adding a provenance check for licensed content to the gate [5].

As of October 2026, NIST says AI RMF 1.0 is being revised under the White House AI Action Plan [4]. Cite version 1.0 by name in your policy, and recheck subcategory identifiers before each annual review.

For a fuller control set aimed at the RMF alone, see NIST AI RMF controls for acquired training data. To write the gate into policy text, start from the AI training data governance policy template.

EU AI Act Article 10 and Annex IV: what the gate must capture

If a model may end up in a high-risk system, the gate must capture the facts Article 10 asks providers to govern. Article 10 requires training, validation and test sets for high-risk systems to be subject to data governance and management practices. Those practices cover design choices, data collection processes and data origin, preparation such as annotation, labelling, cleaning and enrichment, assumptions, and examination of possible biases. They also cover identification of data gaps and how the gaps are addressed [6]. Annex IV point 2(d) asks for datasheets that describe the training data, including provenance, scope, main characteristics, and labelling and cleaning methods [9].

Timing changed this year. Regulation (EU) 2026/1744, the Digital Omnibus on AI, was published in the Official Journal on 24 July 2026 [7]. As of October 2026, commentary reports that the high-risk application dates moved to 2 December 2027 for Annex III systems and 2 August 2028 for Annex I systems [8], and Regulation (EU) 2026/1744 also amends Article 10's data governance practices [7]. A later date is not a reason to skip the evidence. Provenance has to be collected when the data comes in, because it cannot be rebuilt reliably afterward.

General-purpose model providers face a separate duty. Article 53 has required a copyright compliance policy and a public summary of training content since 2 August 2025 [10]. The register below supplies inputs to that summary. The statute-level detail lives in our Article 10 data governance guide, the EU AI Act licensing overview and the guide to EU AI Act training data summaries and what to ask suppliers.

Designing the training data register

The register is the auditable record of approvals, so every field should answer a question an auditor, regulator or litigant will ask. Keep one row per licensed dataset version, not per vendor. Link each row to the model lineage system so you can answer "which models touched this data" without searching.

Illustrative example: invented to show structure; it does not describe an available dataset.

register_id: TDR-2026-0412
dataset_name: "Field service work orders, 2019-2025, v2"
source_description: "Operational records from a US equipment servicer"
licensor: "Licensing entity named in executed agreement"
license_ref: "contracts/DLA-2026-031.pdf"
permitted_uses: [sft, evaluation]        # copied from license, not paraphrased
prohibited_uses: [redistribution, rag_external]
territory: worldwide
term_end: 2029-06-30
deletion_duty: "Delete raw records at term end; trained weights retained per clause 9"
personal_data_status: de_identified
deid_method: "Direct identifiers replaced with tokens; free text redacted; sample of 500 checked"
special_categories: none_detected
high_risk_screen: "Not used in Annex III system (owner attestation 2026-09-30)"
documentation: ["docs/datasheet-v2.md", "metadata/croissant-rai.json"]
owner: "j.doe (Applied ML)"
approved_by: ["privacy", "counsel", "governance"]
approval_date: 2026-10-02
models_trained: ["support-agent-ft-3.1", "eval-suite-q4"]
next_review: 2027-04-01

Three fields cause the most trouble later. permitted_uses should copy the license wording, since a paraphrase is how scope creep starts. deletion_duty has to say what survives term end, because raw records and trained weights are often treated differently. models_trained must be updated by the pipeline, not by hand. Otherwise it goes stale within a quarter.

Documentation formats to request or produce

Ask for documentation in a format your tooling can read, and produce it yourself if the supplier cannot. Datasheets for Datasets and Data Cards remain the common human-readable formats. Croissant-RAI extends MLCommons Croissant with machine-readable responsible-AI fields that build on both [11]. NeurIPS 2026 requires a minimal set of Croissant RAI fields, including limitations, biases and intended use, for its Evaluations and Datasets Track. That shows the format is in practical use [12].

The Data & Trust Alliance Data Provenance Standards define metadata categories such as source, lineage, legal rights, privacy and protection, data type and intended use [13]. Those categories line up with the register fields above. Check the current version with the Alliance before requiring it in contracts. For enterprise records, our guide to dataset cards for licensed enterprise data shows the fields that matter, and the data source documentation check finds gaps before review.

How sourced operational data fits the gate

Data sourced through an intermediary should arrive with gate evidence attached, not bolted on afterward. When teams buy through a broker, ask who the licensor is and whether rights flow through to you (what to verify with brokers and resellers).

SourceX sources operational datasets from US companies on request, such as support and sales histories, engineering records, documents, and finance and legal workflows, and manages the licensing process. Every dataset is rights-reviewed for ownership and consents. It is delivered under a license that defines the records, uses, term and delivery, and diligence materials on source, rights, preparation and allowed use are prepared for each dataset. Names, emails, phones and account numbers are removed or replaced before delivery and the method is recorded. A sample is then checked, though no method is perfect. Data is sourced on request rather than held in stock, so a request does not guarantee a match. Governance leads can describe the data and intended use on the SourceX buyer page, and how SourceX handles data governance covers its own side of the process.

Bring governed training data requests to SourceX

If your gate needs licensed operational data with rights review, a written license and per-dataset diligence materials, describe the records and the intended use rather than naming companies. SourceX looks for US businesses that hold that data, and nothing is contracted until a supplier agrees and approves the release. Start a buyer request at sourcex.si/buyers.