User Guide

Register & Sign In

Navigate to http://localhost to reach the login page. Click Register to create your first account, or sign in with existing credentials. All passwords are bcrypt-hashed; credentials never leave your infrastructure.

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Run take_screenshots.py to generate

The login page — enter credentials or click Register to create a new account.

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Run take_screenshots.py to generate

The registration form — fill in username, email, and password to create your account.

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Run take_screenshots.py to generate

After entering credentials, click Sign In to access your workspace dashboard.

Create a Workspace

A Workspace is the top-level container for a masking project. It holds all your database connections, table configurations, data mappings, and job history. Click New Workspace, enter a name and optional description, then click Create.

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The workspace dashboard — lists all your workspaces. Click + New Workspace to create your first project.

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The New Workspace modal — give your project a name and optional description.

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The workspace overview tab — shows a summary of connections, tables, and recent job activity.

Connect Your Databases

Navigate to the Connections tab inside your workspace. You need at least two connections: a source (production-like data) and a destination (target environment for masked data). Supported types include PostgreSQL, MySQL, Azure SQL, MongoDB, and file uploads.

For each connection, fill in:

• Name — a label used in job configuration
• Type — database engine (e.g., POSTGRESQL)
• Host / Port / Database — connection coordinates
• Username / Password — stored encrypted with AES-256
• Role — tick Source or Destination (or both)

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The Connections tab with source and destination databases configured — click + Add Connection to add more.

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The Add Connection form — enter the host, database name, credentials, and role.

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Filling in the source connection — enable the Source role toggle and click Add Connection.

Configure Tables & Column Generators

The Tables tab lets you tell OpenDataMask which tables to process and how to transform each column. For every table, choose a masking mode (MASK, GENERATE, PASSTHROUGH, or SKIP), then configure a generator for each column that should be anonymised.

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The Tables tab listing configured tables — click + Add Table to add a new one.

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Configuring the users table — selecting MASK mode.

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Column generators configured: full_name → FULL_NAME, email → EMAIL, phone → PHONE. Each mapped column will receive realistic synthetic data when the job runs.

Data Mappings (Advanced Column Control)

The Data Mappings tab gives you fine-grained, per-column control that overrides the table-level generator configuration. Use it to set each column to one of three actions:

• Mask — replace the value with a generated fake (choose strategy: FAKE, HASH, NULL, REDACT, PARTIAL_MASK, or REGEX)
• Migrate As-Is — copy the original value unchanged
• Omit — exclude the column entirely from the output

The wizard walks you through three steps: select a connection → select a table → configure each column.

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Step 1 — Choose the connection whose schema you want to configure.

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Step 2 & 3 — Select the table, then set the action and masking strategy for each discovered column. Changes are saved immediately.

Run a Masking Job

With connections and table configuration in place, go to the Jobs tab and click Run New Job. Select your source and destination connections, then click Run. The job runs asynchronously — the progress badge cycles from PENDING → RUNNING → COMPLETED (or FAILED) in real time.

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The Jobs tab — lists all past and running jobs. Click Run New Job to start a masking run.

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The Run New Job modal — select the source and destination connections, then click Run.

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Expanding the job log shows per-table row counts and any warnings raised during masking.

Custom PII Detection Rules

OpenDataMask ships with built-in sensitivity detection for common PII (email addresses, phone numbers, SSNs, etc.). The Sensitivity Rules page under Settings lets you define your own detection rules for domain-specific identifiers — for example, an internal employee ID format or a proprietary customer reference number.

Each custom rule has:

• Name — displayed in scan reports and data mapping labels
• Matchers — column-name patterns and/or regex patterns that identify the PII type
• Data type filter — optionally restrict the rule to text, numeric, or date columns
• Linked preset — optionally auto-apply a masking strategy when the rule fires

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The Sensitivity Rules settings page — lists all built-in and custom PII detection rules.

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The New Rule side panel — give the rule a name and add one or more column-name matchers.

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A custom rule targeting columns named employee_id — the next sensitivity scan will flag matching columns and recommend a masking strategy.