Leadership and readiness

Data governance for data licensing: a stage-by-stage approval and evidence log

By SourceX Editorial · Reviewed by Noah Loul · October 13, 2026

Short answer

This data governance guide gives a stage-by-stage approval matrix and evidence log for licensing company data to an AI developer. For each of eight stages, from inventory to post-release takedown, it names the one accountable approver, who does the work, and the dated, signed record that proves the decision, so a mid-size company can show who decided what.

Key takeaways

Data governance assigns decision rights; for a licensing decision that means one accountable approver per stage, from inventory to takedown.
The data owner decides scope, the steward runs the process and keeps the records, and the system admin executes; export permission is not approval.
Access, privacy permission, ownership and contractual licensing rights are four separate questions with different approvers.
De-identification reduces privacy risk but does not create a right to license the data.
Every approval should produce a dated, signed record tied to the exact dataset released, kept in one evidence file.
Governance continues after release through an obligations register and a tested deletion and takedown procedure.

What is data governance, and what does a licensing decision add?#

Data governance is the authority and control over how a company's data is managed and used: who decides, under what policies, and how decisions are checked. DAMA's DMBOK places it at the center of 11 data management knowledge areas, and ISO/IEC 38505-1 addresses it to governing bodies. The full definition and the internal framework (inventory, sensitivity tiers, rights register, use rules) are covered in the glossary and in our guide to information governance for AI.

An external licensing decision adds what internal programs rarely test: data leaves the company, a counterparty gains contractual rights, and a model cannot unlearn a mistake. That is why it needs its own approval path, set out in the matrix below.

Who holds which role in a licensing decision?#

The data owner decides scope, the data steward runs the process and keeps the records, and the system administrator performs the export. NIST defines an information owner as an official with statutory or operational authority over specified information and responsibility for its controls, usually a business executive rather than IT. See the glossary entry on the data steward for the role definition.

Four separate questions get mixed up here: access, privacy permission, ownership and the contractual right to license. Salesforce, for example, grants Weekly Data Export by default only to the System Administrator profile, which answers whether someone can pull records and nothing else. De-identification reduces privacy risk but does not create a right to license. The full framework is in our guide to data privacy and the right to license business records.

Access: can we technically export it?
Privacy permission: may we use the personal information for this purpose?
Ownership: is it ours, or a customer's or vendor's?
Contractual licensing right: do our agreements allow licensing it to a third party?

01Your work records
02Personal details removed
03You approve the release
04Licensed to AI developers

How it works with SourceX: Your work records → Personal details removed → You approve the release → Licensed to AI developers.Illustrative

Data governance for data licensing: the stage-by-stage approval matrix#

Use this RACI-style matrix to assign who is accountable (A, one per stage), who does the work (R), who must be consulted (C) and who is informed (I) at each stage. If two people are accountable, nobody is. A stage is not complete until the accountable person signs the record in the last column.

The matrix covers who signs at each stage. It does not restate who may bind the company: whether a board or shareholder vote is needed is covered in our guide to board and shareholder approval, and how management, sponsor and board split that authority is covered in the operating partner versus portfolio CEO guide.

Data governance for data licensing: the stage-by-stage approval matrix
Stage	Accountable (A)	Responsible (R)	Consulted (C) / Informed (I)	Approval record
1. Inventory and scoping	Data owner	Data steward; system admin	C: legal/privacy counsel. I: executive sponsor, finance	Signed dataset inventory: systems, objects, fields, date range, record counts
2. Rights review	Legal counsel	Legal counsel; data steward pulls contracts	C: data owner, finance (customer and vendor agreements). I: executive sponsor	Rights memo: agreements reviewed, restrictions found, records excluded
3. Privacy review	Privacy counsel	Privacy counsel; data steward	C: data owner, security. I: executive sponsor	Privacy assessment: personal data categories, jurisdictions, legal position, conditions
4. De-identification sign-off	Privacy counsel	Party performing de-identification; data steward checks samples	C: data owner, security. I: legal counsel	De-identification report: method, fields removed or transformed, residual risk, sample review
5. Security review	Security lead	System admin	C: data steward, legal counsel. I: data owner	Transfer plan: method, encryption, access list, logging, deletion terms
6. Commercial terms	Executive sponsor	Finance lead; legal counsel drafts	C: data owner, privacy counsel. I: board if your governance requires it	Term sheet or agreement: scope, permitted uses, exclusivity, term, audit and deletion clauses
7. Release approval	Executive sponsor	Data steward assembles the evidence file	C: legal counsel, privacy counsel, security. I: data owner, system admin	Release authorization naming the exact files, hashes or manifest approved
8. Post-release monitoring and takedown	Data owner	Legal counsel tracks obligations; system admin executes deletions	C: privacy counsel, security. I: executive sponsor, finance	Obligations register, notices received, takedown or deletion confirmations

What evidence should each approval record?#

Each approval should leave a dated record that a stranger could read in two years and understand what was decided, by whom, on what basis, and about exactly which data. That is the test an acquirer's diligence team, an auditor or a regulator will apply. DCAM's practice of pairing every capability with an evidence artifact is the right model here.

Keep the evidence file in one place controlled by the data steward, with each document versioned and linked to the dataset manifest. Do not rely on email threads as the record.

What evidence should each approval record?
Record	Minimum contents	Signed by	Why it matters later
Dataset inventory	Source systems (for example Zendesk tickets, Jira issues, NetSuite transactions), objects and fields, date range, record counts, known gaps	Data owner	Proves what was in scope and what was not
Rights memo	List of customer, vendor and platform agreements reviewed, relevant clauses, exclusions applied, open questions	Legal counsel	Shows the licensing right was checked, not assumed
Privacy assessment	Personal data categories, jurisdictions involved, purpose analysis, conditions on release	Privacy counsel	Documents the privacy basis for a new use
De-identification report	Method used, fields removed or transformed, residual risk assessment, sample review results, who performed it	Privacy counsel, with the performing party	Supports any de-identification claim; some regimes require documented methods and results
Transfer and security plan	Delivery method, encryption, recipients, access logging, retention and deletion terms	Security lead	Shows the data left under controlled conditions
Executed agreement and term summary	Permitted uses, prohibited uses including re-identification, exclusivity, term, audit and deletion rights	Executive sponsor and legal counsel	Defines what the counterparty may do and what you can enforce
Release authorization	Final manifest or file hashes, date, approver, reference to every record above	Executive sponsor	Ties the decision to the exact bytes released
Obligations register	Notice dates, renewal and deletion deadlines, takedown requests and their resolution	Data owner	Keeps post-release duties from lapsing

How do you run the approvals without stalling the deal?#

Run stages 1 through 3 in parallel where possible, make the data steward the single coordinator, and set stop conditions in advance so a blocking finding ends the process quickly instead of dragging on.

Inventory, rights review and privacy review can overlap once the dataset inventory is drafted: counsel needs to know which systems and fields are in play, not the final record count. De-identification sign-off depends on the privacy review, security review depends on knowing what will be transferred, and release approval depends on everything else.

Write down, before you start, the findings that end or narrow the scope. Typical stop conditions: a customer agreement that prohibits use of customer data beyond service delivery, a platform term that restricts export use, records involving a regulated category your counsel will not clear, or a de-identification method that cannot reach an acceptable residual risk. Narrowing scope (excluding a customer segment, a date range or a field) is usually faster than arguing a blocked category through.

Name one person per role per dataset before stage 1 begins.
Give each stage a target duration and an escalation path to the executive sponsor.
Record dissents. A consulted party who disagrees should have that recorded in the evidence file, not lost.
Re-open earlier stages when scope changes. A new field or date range means a fresh inventory entry and a check by counsel.

What governance obligations continue after release?#

Release is not the end of the decision. Post-release governance means tracking the counterparty's contractual obligations, handling takedown or deletion requests, and having a contingency process if something goes wrong.

Some legal regimes make the contract part of governance. California's CCPA, for example, treats information as deidentified only if the business takes reasonable measures against re-identification, publicly commits to keep it deidentified, and contractually obligates any recipients to comply with those requirements. The HIPAA Privacy Rule's expert determination method requires the expert to document the methods and results that justify the determination. Whether either applies to your data is a question for counsel, but both show why the agreement and the de-identification report belong in the evidence file.

NIST's AI RMF frames the same idea from the AI side: GOVERN 6.1 calls for policies on risks from third-party entities, including infringement of intellectual property or other rights, and GOVERN 6.2 calls for contingency processes for incidents in third-party data deemed high-risk. For a supplier, that translates into an obligations register owned by the data owner, a named contact at the counterparty, and a tested procedure for requesting deletion and receiving confirmation.

How does this apply when licensing data through SourceX?#

SourceX is the enterprise data transaction layer for AI, running sourcing and qualification, rights review, contracting, data preparation, delivery and payment for businesses licensing operational data to AI developers. Your internal governance still decides; the matrix above tells you who signs at each stage.

The first step is a short assessment with no data uploads. The company approves exactly what is released (stage 7), and de-identification is performed by SourceX directly or through its processing partner, so privacy counsel's stage 4 sign-off reviews that work. Large deliveries stay in the seller's storage or ship on encrypted drives, which the security lead reviews at stage 5. SourceX works with U.S., Canadian and EU companies that had 50 or more full-time employees at their peak. If the buyer side of the approvals matters, see the internal approvals guide for AI training data purchases.

Frequently asked questions

What are the main data governance roles?

The core roles are an executive sponsor, data owners who are accountable for specific datasets, data stewards who run processes and keep records, and data custodians or system administrators who operate the systems. Legal, privacy and security functions hold specific approval rights. Titles vary by company, but each role should map to a named person for each dataset.

What is the difference between a data governance framework and a data governance policy?

A framework is the overall structure: roles, decision rights, processes and standards, often borrowed from references such as DAMA-DMBOK, DCAM or NIST frameworks. A policy is a specific written rule inside that framework, such as who may approve sharing data with third parties. A licensing decision path is one process within the framework, supported by one or more policies.

Who should approve licensing company data to an AI developer?

One accountable approver per stage: the data owner for scope, legal counsel for contractual rights, privacy counsel for privacy and de-identification, the security lead for transfer, and the executive sponsor for commercial terms and final release. Some companies also require board or shareholder approval under their governing documents or financing agreements, which counsel should confirm.

Does de-identifying data mean we are allowed to license it?

No. De-identification reduces privacy risk but does not create a right to license. Customer agreements, vendor or platform terms and confidentiality obligations can still restrict use of a de-identified copy, so a rights review is a separate step. Counsel reviews the specific agreements and facts.

Does a system administrator's export permission authorize sharing the data?

No. Export permissions, like Salesforce's Weekly Data Export permission, control who can technically pull data from a system. They do not establish ownership, privacy permission or a contractual right to license the data, which require separate review and approval.

Sources

DAMA International organizes data management into 11 knowledge areas with data governance at the center. Source
ISO/IEC 38505-1 applies the ISO/IEC 38500 governance model to data and gives governing bodies principles for the effective, efficient and acceptable use of data. Source
DCAM version 3 organizes data management into capabilities and sub-capabilities, including data governance, and provides evidence artifacts and scoring criteria for each sub-capability. Source
NIST AI RMF 1.0 GOVERN 2.1, 2.3, 6.1 and 6.2 cover documented roles, executive responsibility for AI risk decisions, third-party rights risks and contingency processes for third-party data. Source
NIST defines an information owner as an official with statutory or operational authority over specified information and responsibility for its controls. Source
Under HIPAA's expert determination method, the expert documents the methods and results that justify the de-identification determination. Source
The CCPA treats information as deidentified only with reasonable measures, a public commitment not to re-identify, and contractual obligations on recipients. Source
Salesforce grants the Weekly Data Export permission by default only to the System Administrator profile. Source

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