Production-grade test data.
Without the production risk.
Pseudon gives engineering, QA, and analytics teams the data they need in minutes — generated synthetically, irreversibly masked, delivered as lightweight virtual copies, and governed from one control plane.
Three bottlenecks, one platform
Test data is usually slow to get, risky to copy, and expensive to store. Pseudon addresses all three at once, instead of trading one for another.
Ship faster
Self-service provisioning turns a multi-day ticket into a minutes-long request. Developers and testers refresh their own environments instead of waiting in a DBA queue.
Stay compliant
Sensitive values are discovered and replaced before data leaves production. Non-production environments hold realistic data that carries no personal information.
Cut storage cost
Virtual copies share a single compressed source block store, so ten teams can each hold a full-size dataset without ten full-size footprints.
Four capabilities that work as one system
Each capability stands on its own — and each one is stronger because the others are there. Mask once and every virtual copy inherits it; generate synthetically where no source exists; govern all of it from a single plane.
Realistic data, even when there is no data
New features, new markets, and new tables rarely have production data behind them. Pseudon generates statistically realistic records that match your schema, so teams can build and test before the first real row exists.
Useful when production data is unavailable, insufficient, legally out of bounds, or simply doesn't yet contain the scenario you need to test.
- ✓Schema & relationship discovery. Pseudon reads table structures, keys, and constraints, then generates data that obeys them.
- ✓Referential integrity across sources. A generated customer keeps the same identity across every related table, database, and file.
- ✓Edge and negative scenarios. Deliberately produce boundary values, malformed inputs, and rare combinations that production data almost never contains.
- ✓Per-scenario data sets. Shape volume, distribution, locale, and business rules per application, feature, or test suite.
- ✓Governed like everything else. Synthetic sets sit in the same catalogue, under the same policies and access controls, as masked data.
Irreversible masking that keeps data usable
Pseudon replaces sensitive values with realistic, fictitious ones that behave like the originals — same format, same distribution, same joins — so applications and tests keep working while the personal data is gone for good.
Identify → Transform → Deliver. Discovery finds what's sensitive, policies decide how it's replaced, and delivery hands the protected copy to the environment that asked for it.
- ✓Automated sensitive-data discovery. Scans schemas and files for names, contact details, government IDs, card numbers, PII, and PHI — and flags what your rules missed.
- ✓Low-code masking policies. Build a policy once, apply it enterprise-wide. No per-database scripts to maintain.
- ✓Consistent across every source. The same input masks to the same output in Oracle, SQL Server, a warehouse, and a flat file — so cross-system joins survive.
- ✓Format-preserving transforms. Masked values keep length, character class, and checksum rules, so validation logic still passes.
- ✓Irreversible by design. Masking is a one-way transformation, not encryption — there is no key that turns the copy back into personal data.
Full-size environments, a fraction of the footprint
Pseudon syncs from your sources, keeps a continuous record of changes, and provisions virtual point-in-time copies that read and write like physical ones. A terabyte-scale environment can be stood up without a terabyte-scale copy.
Treat data the way you already treat code — refresh, rewind, bookmark, branch, and version it, all through the API or the console.
- ✓Sync. Connect to production or a replica and pull an initial compressed copy without disrupting the source.
- ✓Maintain. Capture ongoing changes to build a continuous, immutable timeline you can provision from at any point.
- ✓Deliver. Spin up virtual copies in minutes that share underlying blocks instead of duplicating them.
- ✓Rewind and bookmark. Reset a broken test environment to a known-good moment, or share a bookmarked state with the developer who has to reproduce the bug.
- ✓API-first. Every action is scriptable, so environment refresh becomes a pipeline stage rather than a request form.
One plane for policy, access, and proof
Data Control is where platform and security teams see everything at once: which environments exist, what data is in them, whether it's been masked, and who can touch it. Developers see a catalogue and a button.
Self-service for the people who need data; visibility and evidence for the people accountable for it.
- ✓Self-service catalogue. Teams request, refresh, and release their own environments within the limits you set.
- ✓Role-based access control. Scope every dataset and action by team, project, and environment tier.
- ✓Sensitive-data visibility. See where sensitive data lives across on-prem and cloud, and which copies are still unprotected.
- ✓Masking-coverage tracking. Report on what is masked, by which policy, and when — the evidence auditors ask for.
- ✓Pipeline integrations. Trigger provisioning and refresh from your existing CI/CD, IaC, and ITSM tooling.
From production source to safe environment, in four steps
Connect
Point Pseudon at your databases, warehouses, and file stores. It takes a compressed initial copy and then tracks changes continuously.
Discover & protect
Automated scans classify sensitive fields. Masking policies replace them with realistic fictitious values; synthetic generation fills gaps where no source data exists.
Provision
Teams pull virtual, protected copies on demand from the catalogue or straight from a pipeline — full-size datasets, minimal storage.
Govern
Access, policies, coverage, and audit history stay centralised, so speed for developers never means blind spots for security.
What changes on day one
The comparison below describes the shift teams typically make when they move off a manual test-data process. Actual results depend on your data estate — we'll size it with you during a pilot.
Scroll the table sideways to see the full comparison →
| Dimension | Manual test-data process | With Pseudon |
|---|---|---|
| Provisioning | Ticket to a DBA; days or weeks of waiting. | Self-service from a catalogue or pipeline, in minutes. |
| Storage | Every team holds its own full physical copy. | Virtual copies share a compressed block store. |
| Compliance risk | Production data lands in non-production, or is scripted ad hoc. | Policy-driven masking applied before delivery, every time. |
| Coverage | Subsets miss edge cases; new features have no data at all. | Full datasets plus synthetic generation for gaps and edge cases. |
| Refresh cadence | Rare, because refreshes are expensive and manual. | On demand — refresh, rewind, or branch as often as needed. |
| Audit evidence | Assembled by hand ahead of each audit. | Continuous coverage reporting and access history. |
Built for regulated, data-heavy delivery teams
Banking & financial services
Card, account, and KYC data protected before it reaches development, with referential consistency across core and satellite systems.
Healthcare & insurance
PHI and claims data de-identified for testing and analytics while keeping clinical and actuarial patterns intact.
Retail & e-commerce
Realistic order, catalogue, and customer volumes for load testing and peak-season readiness — without exporting real shopper data.
Enterprise IT & SaaS
Fast, repeatable environment refresh for CI/CD, multi-tenant testing, migrations, and AI/ML experimentation.
Common questions
What exactly is Pseudon?
Pseudon is FabLogics' test data management platform. It covers four things end to end: generating synthetic data, masking sensitive data, automating the delivery of lightweight virtual data copies, and governing all of it from one control plane.
What is the difference between masking and synthetic data?
Masking starts from your real data and irreversibly replaces the sensitive values, so the structure, volume, and relationships of production are preserved. Synthetic data is generated from scratch against your schema, for cases where there is no usable source data at all — a new feature, a rare scenario, or a dataset you are not permitted to copy. Most teams use both.
Does Pseudon change anything in my production systems?
No. Pseudon reads from a production source or a replica and does all masking, generation, and provisioning outside it. Production stays read-only from Pseudon's perspective.
Where can Pseudon be deployed?
On-premises, in your cloud account, or as a hybrid of both — with the control plane spanning environments so governance stays central even when the data does not.
Which data sources are supported?
Relational databases including Oracle, SQL Server, PostgreSQL, MySQL, and DB2, along with data warehouses, data lakes, and file-based sources such as CSV and Parquet. If you have a source that isn't on this list, ask us — connector coverage is expanding.
How does it fit into our CI/CD pipeline?
Every action — provision, refresh, rewind, mask, release — is available through the API, so environment setup becomes a pipeline stage. Teams commonly trigger it from their existing build, infrastructure-as-code, and service-management tooling.
Is masked data still useful for testing and analytics?
Yes. Transformations preserve format, length, distribution, and referential integrity, so applications validate the values correctly, joins still resolve, and analytical patterns hold. What's removed is the ability to identify a real person.
How does an engagement start?
With a scoped pilot on one application and its data sources. We connect a source, run discovery, apply a masking policy, and provision a working environment — so you can measure the change against your current process before committing further. Call us on 90922 00225 to arrange it.
See Pseudon on your own data
Tell us which application is hardest to get data for, and we'll walk you through a pilot on that one first.