Who should use the Data Masking workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
5-step pipeline — follow each step in order
1Setup · Step 1 of 5
Open task pageSensitive Data Masking
Prepare inputs and settings through Sensitive Data Masking before running data masking.
Why this matters
Sensitive Data Masking sets up the foundation for data masking; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
2Input · Step 2 of 5
Open task pageGenerate synthetic data
Use Generate synthetic data to build supporting assets that improve data masking quality.
Why this matters
Generate synthetic data strengthens data masking by feeding better supporting material into the pipeline.
What you get
Supporting assets from generate synthetic data are prepared and connected to the main workflow.
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3Core · Step 3 of 5
Open task pageData Masking
Execute data masking with Data Masking to produce the primary visual asset.
Why this matters
This is the core step where data masking actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass visual asset is generated and ready for refinement in the next steps.
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4Refine · Step 4 of 5
Open task pageOrchestrate data workflows
Refine and validate data masking output using Orchestrate data workflows before final delivery.
Why this matters
Orchestrate data workflows adds quality control so issues are caught before the workflow is finalized.
What you get
The visual asset is improved, validated, and prepared for final delivery.
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5Deliver · Step 5 of 5
Open task pageData Cleaning
Package and ship the output through Data Cleaning so data masking reaches end users.
Why this matters
Data Cleaning is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized visual asset is ready for publishing, handoff, or integration.
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