AI Training Data Governance Policy: A Template for Licensed and Third-Party Data

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

Why acquired data needs its own policy

Acquired data needs its own policy because its permissions come from a contract and a supplier's collection history, not from your own privacy notices. A general data governance policy (see data governance) usually assumes you collected the data and set its purposes; licensed corpora arrive with someone else's consents, field definitions and restrictions. The failure mode is familiar: a support-ticket corpus licensed for evaluation drifts into a fine-tuning run because nothing in the pipeline records the license scope.

Frameworks already expect this. NIST's AI RMF places policies for third-party data and software under its GOVERN function [1], and ISO/IEC 42001 requires an AI management system with defined controls that a data policy can implement [2]. For high-risk systems in the EU, Article 10 requires training, validation and test sets to be subject to data governance and management practices [3]. For the regulatory map behind these, start at the AI training data compliance hub.

Scope: which data and which uses the policy covers

The policy should cover every dataset used in model development, defined by use rather than by source. Name four use classes explicitly, because licenses and laws treat them differently:

• Pre-training: large corpora that shape base capabilities; highest disclosure exposure under EU Article 53 and California AB 2013 [4][7].
• Supervised fine-tuning and preference data: smaller, often operational records such as resolved support tickets or code reviews, whose licenses often allow some uses and exclude others.
• Evaluation and test sets: must stay isolated from training to avoid contamination; licenses sometimes allow eval but not training.
• Retrieval (RAG) corpora: content shown to users at inference time, which raises reproduction and display questions training does not (see internal vs customer-facing RAG licensing).

State what is out of scope too, for example synthetic data generated entirely from already-approved datasets, which can inherit the parent dataset's register entry.

The template: policy sections and what each must say

A complete policy has about ten sections, each tied to a record it produces. The outline below can be pasted into your policy system and adapted.

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

Section 7 also needs a statement on whether trained models themselves can contain personal data; EDPB Opinion 28/2024 says model anonymity must be assessed case by case [9].

The approval gate: evidence required before data reaches a training job

The approval gate is the control that makes the policy enforceable: no register ID, no bucket access. Wire it into infrastructure, for example by granting read access on the training data lake only to datasets whose register record shows status approved and an unexpired term.

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

register_id: DS-2026-0147
title: "B2B SaaS support tickets, 2021-2025"
supplier_type: licensed_operational_data
data_steward: j.ortiz
allowed_uses: [sft, eval]          # pre_training and rag not permitted
model_scope: "internal assistant models only"
territory: worldwide
term_end: 2028-06-30
personal_data: true
deid_method: "named-entity replacement plus account-number masking; 500-record manual check"
legal_basis_note: "supplier contract and customer terms reviewed; see privacy review PR-311"
third_party_content: "attachments excluded at source"
disclosure_fields_captured: [source_type, date_range, personal_data_flag, copyright_status]
deletion_trigger: [term_end, supplier_notice]
approvals: {legal: 2026-09-12, privacy: 2026-09-15, security: 2026-09-10}
status: approved

Each field maps to a question your approvers already ask. The privacy approver should test three questions per dataset: what the legal basis is, whether training is compatible with the purpose the data was originally collected for, and what the consent or license actually covers [11]. The data provider due diligence questionnaire collects most of this evidence from suppliers in one pass. When you source through an intermediary such as SourceX for AI data buyers, ask for the per-dataset diligence materials and map them straight into these fields.

Personal data and de-identification rules

The policy should set one de-identification standard per jurisdiction and require proof it was applied, not just a supplier's assurance. Under the GDPR, only information that is anonymous, judged against means reasonably likely to be used to identify someone, falls outside data protection rules [8]. Under the CCPA, information counts as deidentified only if the holder takes reasonable measures against re-identification, publicly commits not to re-identify, and contractually binds recipients to the same [10].

Write those conditions into the policy as obligations on your own teams: no joining licensed records to internal CRM data, no re-identification attempts outside sanctioned red-team tests, and downstream contract flow-down when data moves to a vendor such as an annotation provider. Health data needs its own clause naming HIPAA Safe Harbor or Expert Determination.

Copyright and license controls

For general-purpose model providers serving the EU, a written copyright policy is a legal requirement, so your data policy should either contain it or point to it. Article 53(1)(c) requires a policy to comply with Union copyright law, including honoring rights reservations under the DSM Directive [4], and the Code of Practice copyright chapter describes drawing up, maintaining and implementing that policy [5]. The GPAI Code of Practice copyright chapter guide covers how licensed datasets fit in.

Two license-specific controls belong in the policy regardless of jurisdiction. First, a rule on embedded third-party content (quoted emails, attachments, stock images inside documents) that the supplier may not own. Second, a code-specific rule screening licensed repositories for GPL and AGPL files before training (see copyleft contamination in licensed code).

Disclosure outputs the policy must generate

The policy should require that every approved dataset carry the fields public disclosures need, captured at intake rather than reconstructed later. As of October 2026, GPAI providers prepare a public summary of training content using the Commission's July 2025 template [6], and California AB 2013 requires developers of generative AI systems to post documentation about their training data [7]. Capture source type, date range, whether personal information is present, whether copyrighted material is present and whether data was licensed or purchased.

The disclosure requirements comparison shows how one supplier record can feed both. Assign the model documentation owner to assemble disclosures from the register, so no team writes them from memory.

Retention, deletion and exceptions

Retention rules must reconcile two opposing pressures: regulators and auditors want records kept, while licenses and consent withdrawals require data deleted. Separate the dataset copies (subject to license deletion) from the governance records about them (register entries, approvals, deletion certificates), which you typically keep longer. The retention requirements guide covers the trade-offs.

Define what deletion means for derived artifacts: tokenized shards, embedding indexes, eval caches and checkpoints. Retraining is rarely practical, so the policy should state when a model trained on withdrawn data is retired, restricted or documented as an accepted risk, and who approves that. Log every exception with an expiry date and review the log quarterly.

Mapping the policy to NIST, ISO/IEC 42001 and the EU AI Act

A short crosswalk appendix lets auditors test the policy against the frameworks they use. Map sections 3 and 4 to NIST AI RMF GOVERN 6 on third-party risk [1] and to ISO/IEC 42001 Annex A controls on data and suppliers [2]; the NIST AI RMF third-party data guide and the ISO/IEC 42001 Annex A guide give clause-level detail. Map sections 5 and 8 to EU AI Act Article 10 [3] and to the data management procedures that Article 17's quality management system expects.

As of October 2026, Regulation (EU) 2026/1744 reportedly moved high-risk application dates to 2 December 2027 for Annex III systems and 2 August 2028 for Annex I, and also amends Article 10 [13], so the policy can be built now. Keep the evidence in an audit readiness pack and see the guide for AI governance leads for running approvals day to day.

Sourcing licensed data that fits your governance policy

SourceX sources operational datasets from US companies on request, with each dataset rights-reviewed for ownership and consents and delivered under a license defining records, uses, term and delivery. Personal details are removed or replaced before delivery, the method is recorded, and diligence materials are prepared per dataset so your register entries start with evidence. If your policy is ready and you need data to put through it, describe the data you need to SourceX.