Test Data
Test Data Management, On Demand, Built on UiPath Test Cloud. A UiPath Test Cloud capability that gives your team compliant, realistic test data whenever testing needs it, not whenever a manual refresh gets around to it.
The Challenge
Automated testing is only as good as the data behind it. Teams often wait on production copies, work with unrealistic data, or risk exposing sensitive information, all of which blocks high-quality testing.
Right now, teams file a ticket for a production copy, wait for a DBA to extract it manually, locate sensitive fields by hand, scrub values with ad hoc scripts, and load the masked copy into a test environment, where it drifts from production until the next refresh. Provisioning takes days to weeks, manual masking risks leaving sensitive data exposed, and test environments grow stale between refreshes.
From Manual to Agentic
Today, provisioning is slow, manual masking creates compliance exposure, and test data drifts from production between refreshes.
With the solution, robots connect to production sources and profile the data schema. Agents classify sensitive fields against data policy, and once your team approves the masking rules, robots apply them automatically. Agents generate synthetic data to fill coverage gaps while preserving referential integrity across tables, then validate the result against your test coverage needs. Refreshes run on a schedule or on demand from your CI/CD pipeline.
The Impact of On-Demand Test Data Management
Provisioning Time
Minutes, not weeks.
Compliance
Masked by default.
Freshness
Always current.
What's Included
Find out what's included with the test data accelerator
01
Data Discovery and Profiling
Across source systems.
02
Agentic Synthetic Data Generation
Fills coverage gaps while preserving referential integrity.
03
Sensitive Data Masking
Compliance handling built in.
04
Automated Refresh
Scheduled or on demand from your CI/CD pipeline.
FAQs
How is sensitive data identified for masking?
Agents classify PII and regulated fields against your data policy, then your team approves the masking rules before anything runs automatically.
Does synthetic data preserve relationships between tables?
Yes. Agentic generation fills coverage gaps while preserving referential integrity across related tables.
Does this work alongside our existing data governance policy, or replace it?
Alongside. Agents classify sensitive fields against your existing data policy and your team approves the masking rules; the capability enforces the policy at scale rather than defining a new one.
Related Capabilities
Schedule a Consultation
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