---
title: "Secure Data Sharing: How to Share Business Data Safely"
url: https://www.brightinfo.com/blog/secure-data-sharing/
site: https://www.brightinfo.com/
description: "Learn how to share business data securely: assess risk, control access, encrypt transfers, remove PII and prepare data for licensing without losing control."
last_updated: 2026-10-29
---

# Secure Data Sharing: How to Share Business Data Safely

Published 2026-10-29 · Bright Info

Secure data sharing means giving another party the data they need, for a stated purpose, while keeping control of who sees it, what they can do with it and how it travels. In practice that comes down to five things: assess the risk first, share the minimum, remove or transform identifiers, encrypt and restrict access, and put the rules in a written agreement. This guide walks through each step, then shows how it applies when the recipient is a buyer licensing your company data.

## TL;DR

- Start with a risk assessment covering legal, ethical, reputational and commercial exposure, then share only the minimum data the purpose requires.
- Encrypt data in transit, use role-based access with least privilege, and put sharing terms in a written agreement.
- De-identification lowers disclosure risk but does not erase it, so test the result and keep contractual limits on re-identification.
- Before licensing data, document its source, quality, permitted uses and who may touch it.
- If you plan to license company data, move nothing until a deal is signed, and use an independent de-identification step you have approved.

## Table of Contents

- [What secure data sharing actually covers](#what-secure-data-sharing-actually-covers)
- [Step 1: Assess the risk before you share anything](#step-1-assess-the-risk-before-you-share-anything)
- [Step 2: Share the minimum, and write down why](#step-2-share-the-minimum-and-write-down-why)
- [Step 3: Protect data in transit and at rest](#step-3-protect-data-in-transit-and-at-rest)
- [Step 4: Control who can access what](#step-4-control-who-can-access-what)
- [Step 5: Put the terms in writing](#step-5-put-the-terms-in-writing)
- [Sharing data without exposing PII](#sharing-data-without-exposing-pii)
- [Crossing borders: one note for global readers](#crossing-borders-one-note-for-global-readers)
- [Preparing data for licensing](#preparing-data-for-licensing)
- [Our perspective: sell the data, keep the control](#our-perspective-sell-the-data-keep-the-control)
- [See what your data could be worth](#see-what-your-data-could-be-worth)
- [A practical checklist before any data leaves](#a-practical-checklist-before-any-data-leaves)
- [FAQ](#faq)
- [Sources](#sources)

## What secure data sharing actually covers

The phrase gets used for two different things. The first is a technical feature: a platform lets another account query your data without a copy leaving your environment. The second is a discipline: deciding what to share, with whom, under what terms, and with what protections along the way. The technical feature is useful, but it only answers one question, which is how the data moves. It says nothing about whether you should share it, whether it contains personal information or what the recipient may do with it afterward.

A secure sharing program answers all of them. That is why the guidance from regulators and standards bodies reads less like a product manual and more like a checklist: purpose, minimization, protection in transit, access, agreement, monitoring and deletion.

## Step 1: Assess the risk before you share anything

The Open Data Institute's guide to [assessing risks when sharing data](https://theodi.hacdn.io/media/documents/Assessing-risks-when-sharing-data-a-guide-OPEN-1.pdf) groups risk into four categories: legal and regulatory, ethical, reputational and commercial. That framing is useful for a business because each category has a different owner inside the company. Legal worries about regulation and contracts. Communications worries about reputation. Finance worries about commercial value leaking out. Ethical risk, such as harm to the people whose information is in the data, belongs to everyone.

The same guide makes a point that is easy to miss: risk appetite differs by context, and the goal is to evaluate likelihood and severity with a risk matrix, not to chase zero risk. A practical version for a mid-sized company looks like this:

| Question | Why it matters |
| --- | --- |
| What kinds of data are in the set? | Email, chat, CRM and support tickets mix business and personal content |
| Who could be harmed if it leaked? | Customers, employees, partners, your own company |
| How likely is exposure at each step? | Export, transfer, storage at the recipient, reuse |
| How severe would exposure be? | Regulatory action, contract breach, lost trade secrets |
| What controls reduce the likelihood? | Minimization, de-identification, encryption, access limits |

If the data includes personal information, the UK regulator's [data sharing checklist](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/data-sharing-a-code-of-practice/annex-a-data-sharing-checklist/) asks whether sharing is justified, necessary and proportionate, what the minimum data required is, and what the benefits and risks are to individuals or society. It is a UK document, but the questions travel well. They are the ones any US counsel will want answered too.

## Step 2: Share the minimum, and write down why

Minimization is the cheapest security control you have. Data you never send cannot leak from the recipient's systems. Before any export, define the purpose in one sentence, then list the fields, date ranges and systems that purpose actually needs.

A few habits make this concrete:

- **Scope by purpose, not by convenience.** An export of an entire mail archive is easy. An export limited to specific mailboxes and date ranges is safer and often just as valuable.
- **Exclude categories outright.** Health information, government IDs, payment card numbers and credentials rarely belong in a shared set. Strip them at the source.
- **Record the decision.** A short log of what was included, what was excluded and who approved it saves weeks when a buyer, auditor or regulator asks later.

## Step 3: Protect data in transit and at rest

Once you know what is leaving, the question is how. The UK National Cyber Security Centre's design pattern on [safely exporting data](https://www.ncsc.gov.uk/guidance/design-pattern-safely-exporting-data) recommends encrypting data in transit, for example with TLS, and encrypting the objects themselves so the information stays protected until it reaches the intended recipient. Object-level encryption matters because a file sitting in a transfer folder is exposed to whoever can read that folder, even if the connection that delivered it was encrypted.

The NCSC also advises keeping the management functions of an export solution separate from your internal systems, so a compromised internal device cannot tamper with the export process. For a company, that means the person or machine that approves and releases an export should not be the same login that browses everyday email.

The UK regulator's guidance on [bulk transfers of personal information](https://ico.org.uk/for-organisations/advice-and-services/audits/data-protection-audit-framework/toolkits/data-sharing/bulk-transfers-of-personal-information-in-databases-or-lists/) lists measures that apply to any large export: encryption in transit, written sharing agreements, secure transfer protocols such as SFTP, and limiting release to authorized staff. Email attachments and consumer file-sharing links are not on that list for a reason.

Documents deserve the same care as databases. If you pass contracts or reports as PDFs, [locking PDFs before sharing](https://blog.flowpdf.free/blog/prevent-editing-pdf) keeps recipients from altering them, and doing it locally means the file never has to pass through a third-party upload tool first.

## Step 4: Control who can access what

Encryption protects the pipe. Access control protects everything at either end. The ICO's [information security toolkit](https://ico.org.uk/for-organisations/advice-and-services/audits/data-protection-audit-framework/toolkits/data-sharing/information-security/) points to encrypting confidential information, setting role-based access levels, documenting sharing methods and security standards in agreements, and regularly testing access controls and watching for anomalies.

The US federal [Zero Trust Data Security Guide](https://resources.data.gov/assets/documents/Zero-Trust-DataSecurityGuide_RevisedMay2025_CIO.govVersion.pdf), revised in May 2025, treats role-based and attribute-based access control as central to protecting data during transfer and access. In plain terms: nobody gets access because of where they sit on the network, and every request is checked against who is asking, for what and under which conditions.

Texas's Department of Information Resources adds a governance angle in its [data sharing best practices](https://dir.texas.gov/sites/default/files/2023-07/Data%20Sharing%20Best%20Practices_1.pdf). Publishing should be requested and tracked through a standard business process, run by a small central team that assigns editing and publishing roles on the principle of least privilege. That is a state agency's guidance, but the pattern fits any company: one front door for sharing requests, a short list of people who can release data, and a record of every release.

## Step 5: Put the terms in writing

A data-sharing agreement is where security becomes enforceable. The ICO describes these agreements as good-practice tools that set out the purpose of sharing, what happens to the data at each stage, the standards that apply, roles and responsibilities, and rules for retention and deletion. See its page on [data sharing agreements](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/data-sharing-a-code-of-practice/data-sharing-agreements/).

Whatever the form, an agreement for sharing business data should answer these questions:

1. What exactly is the data, and what is the permitted purpose?
2. May the recipient pass it to anyone else, and under what conditions?
3. What security standards must the recipient meet, and can you verify them?
4. Is re-identification of de-identified data prohibited?
5. What happens at the end: return, deletion, certification of deletion?
6. Who is notified, and how fast, if something goes wrong?

The ICO also stresses that data protection should be built into the design of a sharing scheme from the start, not bolted on as a compliance checkbox, per its guidance on [smart data schemes](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/smart-data-schemes-and-data-protection/). That is the right order of operations: design the controls, then share.

## Sharing data without exposing PII

Most business data is messier than it looks. A sales team's email history carries customer names, phone numbers and signature blocks. Support tickets contain account details. Slack channels hold casual mentions of people's health, families and salaries. If the recipient does not need that, it should not travel.

![Business communication records with details obscured](https://www.brightinfo.com/assets/blog/secure-data-sharing/image-1.jpg)

NIST's [SP 800-188](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-188.pdf) on de-identifying datasets, published in 2023, describes three broad approaches: removing identifiers, transforming quasi-identifiers (details that can point to a person when combined, such as job title plus city plus date), and generating synthetic data. It frames de-identification as removing the association between data and the people it describes, to reduce disclosure risk, and it tells organizations to evaluate the risk of releasing the de-identified result.

That last instruction is the important one. De-identification reduces risk; it does not abolish it. Free text is the hardest case, because a name can hide in a sentence, a nickname or an attachment. So a sound process has layers:

- **Automated detection and redaction** of obvious identifiers such as names, emails, phone numbers and account numbers.
- **Sampling and human review** of the output to catch what the tooling missed.
- **Contractual limits** forbidding re-identification and restricting onward transfer.
- **A re-check** whenever the dataset, the recipient or the purpose changes.

Who performs the de-identification matters too. If the buyer does it, the buyer has already seen the raw data. An independent party that you have approved, working before any recipient receives anything, removes that problem.

## Crossing borders: one note for global readers

If the data includes personal information about people in other countries, the transfer rules of those countries apply. Under the EU's GDPR, [Article 46](https://www.legislation.gov.uk/eur/2016/679/article/46/adopted?view=plain) requires appropriate safeguards for international transfers, such as standard data protection clauses, binding corporate rules or approved codes of conduct, where there is no adequacy decision. Brazil's data authority has its own [regulation on international transfers](https://www.gov.br/anpd/pt-br/centrais-de-conteudo/outros-documentos-e-publicacoes-institucionais/regulation-on-international-transfer-of-personal-data.pdf/@@display-file/file), issued in 2024, which calls for equivalent protection, accountability and transparency, and limits transfers to the minimum data needed for specific, legitimate purposes. US companies with international customers should ask counsel which regimes their dataset touches before it leaves.

## Preparing data for licensing

Licensing business data is a particular kind of sharing: the recipient is a commercial buyer, the use is defined by a contract, and the data's value depends on how well it is documented. The Texas guidance above offers a good preparation template, even though it was written for public agencies. It asks organizations to document the source and metadata of the data, the approved purposes and access, and the quality, including timeliness and accuracy. It also calls for formal agreements before sensitive, confidential or protected data is shared.

Translated for a company, a licensing-ready data package has:

1. **Provenance.** Which systems produced the data, over what period, and under which terms with your customers and employees.
2. **Rights.** Confirmation that you can license it: customer contracts, privacy notices and employee policies checked for restrictions.
3. **Inventory.** The data types and approximate volumes: email, chat, CRM records, support tickets, documents, call recordings, code or financial exports.
4. **Quality notes.** Gaps, duplicates, format changes and anything known to be wrong.
5. **Sensitivity map.** Where personal, regulated or trade-secret content sits, and how it will be handled.
6. **Access plan.** Who inside your company can export, review and approve.

For more on what buyers look for, see our guide to [how companies license data for AI training](https://www.brightinfo.com/blog/ai-data-licensing/). Teams with structured records may also find our pieces on [procurement data](https://www.brightinfo.com/blog/procurement-data/) and [financial datasets for AI](https://www.brightinfo.com/blog/financial-dataset-ai/) helpful for judging quality before an outreach.

## Our perspective: sell the data, keep the control

Most companies that think about licensing their data worry about the same thing: once it leaves, it is gone. We think that worry is right, and it is why we built the process the way we did.

As a sell-side broker, we work for the data owner, not for buyers. Every qualified buyer is invited to bid, and the bids are sealed, so you compare real offers instead of negotiating with one party who knows you have no alternative. Our commission comes from the winning buyer when a deal closes, so sellers pay us nothing, and we have no reason to favor any particular buyer.

On security, our position is simple. No company data moves until a deal is signed. De-identification is done by an independent partner that you approve, not by the buyer. License terms, including permitted uses, are reviewed and set out transparently before you commit. We describe our approach on our [security and data handling page](https://www.brightinfo.com/security/).

We also think the discipline in this article is the real prize. A company that has done the risk assessment, mapped its sensitive content and documented provenance is in a stronger negotiating position, because buyers can see what they are bidding on and sellers can say no to the wrong terms. Our experience is that data from established systems, such as email, CRM, support and team communications, is what buyers want most, and that sellers who prepare carefully are the ones who feel in control of the outcome.

> *— Erik*

## See what your data could be worth

If you have a few years of company data in systems like email, Slack, a CRM or a ticketing tool, a free valuation is the lowest-risk way to learn what it might fetch. The intake takes about two minutes, you pay nothing, and no data is moved. We only get paid if a deal closes, and the buyer pays us. You can start at [Bright Info](https://www.brightinfo.com/) or compare approaches in our overview of [data monetization companies](https://www.brightinfo.com/data-monetization-companies/).

We describe typical outcomes as ranging from $100K to $4M, depending on the data, its volume and its history. Your result depends on your own dataset, which is why the valuation looks at what you actually hold.

## A practical checklist before any data leaves

Use this as a final gate, whether the recipient is a partner, a vendor or a licensing buyer.

1. The purpose is written down and the data is scoped to it.
2. Counsel has confirmed you have the right to share it.
3. Personal and regulated content has been removed or de-identified, and the result was sampled and reviewed.
4. The transfer method encrypts data in transit, and files are encrypted too.
5. Access is role-based, limited to named people, and logged.
6. A written agreement covers purpose, onward transfer, re-identification, retention and deletion.
7. Someone is responsible for monitoring and for responding if something goes wrong.
8. The setup is retested when data, recipient or purpose changes.

## FAQ

### What is the most secure way to share data with another company?

There is no single method, but the strongest setups combine minimization, encryption in transit and at the object level, role-based access and a written agreement. Avoid ad hoc email attachments for bulk data. Use a controlled transfer channel such as SFTP or a governed sharing platform, and limit release to authorized staff.

### How do I share data without exposing personal information?

Remove direct identifiers, transform details that could identify someone in combination, or use synthetic data, as NIST describes. Then evaluate the residual risk by sampling the output and reviewing free-text fields. Back it with a contract that bans re-identification and restricts onward sharing.

### Does the GDPR allow data sharing?

It can, if there is a lawful basis and the sharing is necessary and proportionate. Transfers outside the EU also need safeguards such as standard data protection clauses where there is no adequacy decision. Ask qualified counsel about your specific dataset and the countries involved.

### How should I prepare data for licensing?

Document where the data came from, what rights you have to license it, its quality and its sensitive content. Decide who approves exports, and put permitted uses and security standards in a signed agreement. A free valuation can tell you what buyers might offer before you invest in preparation.

## Sources

- [Assessing risks when sharing data, Open Data Institute](https://theodi.hacdn.io/media/documents/Assessing-risks-when-sharing-data-a-guide-OPEN-1.pdf)
- [Data sharing checklist, ICO](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/data-sharing-a-code-of-practice/annex-a-data-sharing-checklist/)
- [Design Pattern: Safely Exporting Data, NCSC](https://www.ncsc.gov.uk/guidance/design-pattern-safely-exporting-data)
- [Bulk transfers of personal information, ICO](https://ico.org.uk/for-organisations/advice-and-services/audits/data-protection-audit-framework/toolkits/data-sharing/bulk-transfers-of-personal-information-in-databases-or-lists/)
- [Information security, ICO](https://ico.org.uk/for-organisations/advice-and-services/audits/data-protection-audit-framework/toolkits/data-sharing/information-security/)
- [Federal Zero Trust Data Security Guide](https://resources.data.gov/assets/documents/Zero-Trust-DataSecurityGuide_RevisedMay2025_CIO.govVersion.pdf)
- [DIR Data Sharing Best Practices, Texas](https://dir.texas.gov/sites/default/files/2023-07/Data%20Sharing%20Best%20Practices_1.pdf)
- [Data sharing agreements, ICO](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/data-sharing-a-code-of-practice/data-sharing-agreements/)
- [Smart Data schemes and data protection, ICO](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/smart-data-schemes-and-data-protection/)
- [De-Identifying Government Datasets, NIST SP 800-188](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-188.pdf)
- [GDPR Article 46](https://www.legislation.gov.uk/eur/2016/679/article/46/adopted?view=plain)
- [Regulation on International Transfer of Personal Data, Brazil ANPD](https://www.gov.br/anpd/pt-br/centrais-de-conteudo/outros-documentos-e-publicacoes-institucionais/regulation-on-international-transfer-of-personal-data.pdf/@@display-file/file)

## Recommended

- [Security and data handling](https://www.brightinfo.com/security/)
- [AI Data Licensing: How Companies License Data for AI Training](https://www.brightinfo.com/blog/ai-data-licensing/)

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More posts: https://www.brightinfo.com/blog/