The best data masking tools replace or hide sensitive values like names, emails and card numbers so teams can use realistic data for testing, analytics or AI without exposing real people. Static masking changes a copy of the data, dynamic masking hides values as they're read, and some tools generate synthetic data instead. Eleven options follow in no particular order.
Copying production data into a test environment is one of the quickest ways to leak personal data. Developers need realistic records, but they rarely need real people's details.
Data masking tools give them the first without the second. The eleven tools below are listed alphabetically and in no particular order, and every description comes from the vendor's own website, read this week. My explainer on what synthetic data is covers the alternative to masking real records.
Data masking tools side by side
I checked the free plan column on each tool's own website, as of Oct 2026. Yes means the site names a free plan, a free version, a free account or an open source edition. Trial only means a time-limited trial. Not stated means the pages I read named neither, even where there's a button inviting you to try it, so check the vendor's page before you sign up.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| Accutive Security | Data protection specialist | Masking within wider data protection | Not stated |
| DataSunrise | Database security platform | Dynamic and static masking | Trial only |
| DATPROF | Test data management | Smaller, privacy-safe test databases | Not stated |
| Delphix | DevOps data platform | Masked data for delivery pipelines | Not stated |
| IBM Optim | Data lifecycle management | Production-like data for AI and testing | Not stated |
| K2view | Data product platform | Masking plus tokenization | Not stated |
| Mage Data | Test data and security platform | Test data in regulated sectors | Not stated |
| Protecto | AI data control plane | Masking data sent to AI | Not stated |
| Redgate Data Masker | Database masking tool | SQL Server and Oracle databases | Trial only |
| Satori | Runtime data security | Masking live data at runtime | Not stated |
| Tonic.ai | Synthetic data platform | Realistic de-identified dev data | Not stated |
Eleven data masking tools to review
Accutive Security
Best for: Security teams who want tokenization and data masking alongside encryption key management and identity security from one specialist.
DataSunrise
Best for: Database owners who want both dynamic and static data masking inside a wider data security and compliance platform, with a free trial.
DATPROF
Best for: Test teams who want masked, subsetted or synthetic test data for software development and testing from one test data platform.
Delphix
Best for: Enterprises who want data masking and synthetic data delivered automatically into DevOps pipelines from a Perforce data automation platform.
IBM Optim
Best for: Large organisations who want to provision and mask production-like data for AI and machine learning training while governing it across its lifecycle.
K2view
Best for: Data teams who want masking, tokenization, synthetic data and test data management on one enterprise data product platform.
Mage Data
Best for: Banks and health organisations who want static masking, discovery and database virtualization for safe, secure test data.
Protecto
Best for: AI teams who want sensitive data masked before it reaches models and agents, using masking that keeps the context intact.
Redgate Data Masker
Best for: SQL Server and Oracle teams who want sensitive data replaced in non-production databases for development and testing, with a free trial.
Satori
Best for: Data teams who want dynamic masking at runtime that redacts fields without changing the underlying data, now part of Commvault.
Tonic.ai
Best for: Developers and AI teams who want to de-identify, subset and synthesize structured data with Tonic Structural for safe development.
Find your starting point
- SQL Server and Oracle databases: Redgate Data Masker or DataSunrise.
- Smaller, safer test databases: DATPROF or Mage Data.
- Masked data in delivery pipelines: Delphix or IBM Optim.
- Masking data sent to AI: Protecto.
- Runtime masking on live data: Satori.
- Masking plus tokenization: K2view or Accutive Security.
- Synthetic and de-identified dev data: Tonic.ai.
Masked data can still be personal data
The ICO's guidance on pseudonymisation says pseudonymised personal data is in scope of data protection law. It describes pseudonymisation as a way of reducing risk and improving security, not a way of transforming personal data to the extent the law no longer applies.
So if masked test data can still be linked back to a person, treat it with the same care as live data. Only data that's anonymised falls outside the law, and that's a much higher bar. The ICO also notes this guidance is under review following the Data (Use and Access) Act.
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Static, dynamic or synthetic?
Static masking creates a masked copy of a database for development and testing. Once it's done, the sensitive values are gone from that copy.
Dynamic masking hides values on the fly, depending on who's asking, while the underlying data stays the same. It suits live systems where some users need the full record and others don't.
Synthetic data generates new records that look real but belong to nobody. It's increasingly popular for AI training and for systems where masking alone isn't enough.
Teams that copy production data into test databases often need database management tools alongside masking.
Questions to settle first
- Which databases and file types hold sensitive data?
- Do we need masked copies, runtime masking, or both?
- Must masked values keep the same format?
- Do relationships between tables need to stay consistent?
- Can it find sensitive fields automatically?
- How will we prove masking ran for audits?
Masking protects data at rest, while this guide to email encryption software covers tools that protect it in transit by email.
Signs your test data is a risk
- Developers use full production copies. Mask them.
- Nobody knows where personal data sits. Run discovery.
- Test environments have weak access controls. Treat them as live.
- AI tools see raw customer data. Mask it first.
Do you make data masking or test data tools? Tool makers can ask to be considered for a featured place on this list.
My plain advice on data masking tools
Start by finding where personal data lives, because you can't mask what you haven't found. Then mask every non-production copy by default, so nobody has to remember to do it.
Keep a short record of what was masked and how, in case anyone asks. If you're also checking AI tools, my guide to GDPR-friendly AI tools for UK businesses helps.
Frequently asked questions
What is data masking?
It's the process of hiding or replacing sensitive values, such as names or card numbers, so data can be used without exposing real people.
Is masked data still personal data?
It can be. The ICO says pseudonymised personal data is in scope of data protection law, so only fully anonymised data falls outside it.
What's the difference between static and dynamic masking?
Static masking creates a permanently masked copy. Dynamic masking hides values in real time while the original data stays unchanged.
Is synthetic data better than masking?
It depends. Synthetic data contains no real records, while masking keeps real structure and relationships. Many teams use both.
Can data masking help with AI projects?
Yes. Some tools mask sensitive data before it reaches AI models and agents, while keeping enough context for useful results.
Are there free data masking tools?
Several vendors offer free trials, which let you test masking on a sample database first.
