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  • Tonic Structural User Guide
  • About Tonic Structural
    • Structural data generation workflow
    • Structural deployment types
    • Structural implementation roles
    • Structural license plans
  • Logging into Structural for the first time
  • Getting started with the Structural free trial
  • Managing your user account
  • Frequently Asked Questions
  • Tutorial videos
  • Creating and managing workspaces
    • Managing workspaces
      • Viewing your list of workspaces
      • Creating, editing, or deleting a workspace
      • Workspace configuration settings
        • Workspace identification and connection type
        • Data connection settings
        • Configuring secrets managers for database connections
        • Data generation settings
        • Enabling and configuring upsert
        • Writing output to Tonic Ephemeral
        • Writing output to a container repository
        • Advanced workspace overrides
      • About the workspace management view
      • About workspace inheritance
      • Assigning tags to a workspace
      • Exporting and importing the workspace configuration
    • Managing access to workspaces
      • Sharing workspace access
      • Transferring ownership of a workspace
    • Viewing workspace jobs and job details
  • Configuring data generation
    • Privacy Hub
    • Database View
      • Viewing and configuring tables
      • Viewing the column list
      • Displaying sample data for a column
      • Configuring an individual column
      • Configuring multiple columns
      • Identifying similar columns
      • Commenting on columns
    • Table View
    • Working with document-based data
      • Performing scans on collections
      • Using Collection View
    • Identifying sensitive data
      • Running the Structural sensitivity scan
      • Manually indicating whether a column is sensitive
      • Built-in sensitivity types that Structural detects
      • Creating and managing custom sensitivity rules
    • Table modes
    • Generator information
      • Generator summary
      • Generator reference
        • Address
        • Algebraic
        • Alphanumeric String Key
        • Array Character Scramble
        • Array JSON Mask
        • Array Regex Mask
        • ASCII Key
        • Business Name
        • Categorical
        • Character Scramble
        • Character Substitution
        • Company Name
        • Conditional
        • Constant
        • Continuous
        • Cross Table Sum
        • CSV Mask
        • Custom Categorical
        • Date Truncation
        • Email
        • Event Timestamps
        • File Name
        • Find and Replace
        • FNR
        • Geo
        • HIPAA Address
        • Hostname
        • HStore Mask
        • HTML Mask
        • Integer Key
        • International Address
        • IP Address
        • JSON Mask
        • MAC Address
        • Mongo ObjectId Key
        • Name
        • Noise Generator
        • Null
        • Numeric String Key
        • Passthrough
        • Phone
        • Random Boolean
        • Random Double
        • Random Hash
        • Random Integer
        • Random Timestamp
        • Random UUID
        • Regex Mask
        • Sequential Integer
        • Shipping Container
        • SIN
        • SSN
        • Struct Mask
        • Timestamp Shift Generator
        • Unique Email
        • URL
        • UUID Key
        • XML Mask
      • Generator characteristics
        • Enabling consistency
        • Linking generators
        • Differential privacy
        • Partitioning a column
        • Data-free generators
        • Supporting uniqueness constraints
        • Format-preserving encryption (FPE)
      • Generator types
        • Composite generators
        • Primary key generators
    • Generator assignment and configuration
      • Reviewing and applying recommended generators
      • Assigning and configuring generators
      • Document View for file connector JSON columns
      • Generator hints and tips
      • Managing generator presets
      • Configuring and using Structural data encryption
      • Custom value processors
    • Subsetting data
      • About subsetting
      • Using table filtering for data warehouses and Spark-based data connectors
      • Viewing the current subsetting configuration
      • Subsetting and foreign keys
      • Configuring subsetting
      • Viewing and managing configuration inheritance
      • Viewing the subset creation steps
      • Viewing previous subsetting data generation runs
      • Generating cohesive subset data from related databases
      • Other subsetting hints and tips
    • Viewing and adding foreign keys
    • Viewing and resolving schema changes
    • Tracking changes to workspaces, generator presets, and sensitivity rules
    • Using the Privacy Report to verify data protection
  • Running data generation
    • Running data generation jobs
      • Types of data generation
      • Data generation process
      • Running data generation manually
      • Scheduling data generation
      • Issues that prevent data generation
    • Managing data generation performance
    • Viewing and downloading container artifacts
    • Post-job scripts
    • Webhooks
  • Installing and Administering Structural
    • Structural architecture
    • Using Structural securely
    • Deploying a self-hosted Structural instance
      • Deployment checklist
      • System requirements
      • Deploying with Docker Compose
      • Deploying on Kubernetes with Helm
      • Enabling the option to write output data to a container repository
        • Setting up a Kubernetes cluster to use to write output data to a container repository
        • Required access to write destination data to a container repository
      • Entering and updating your license key
      • Setting up host integration
      • Working with the application database
      • Setting up a secret
      • Setting a custom certificate
    • Using Structural Cloud
      • Structural Cloud notes
      • Setting up and managing a Structural Cloud pay-as-you-go subscription
      • Structural Cloud onboarding
    • Managing user access to Structural
      • Structural organizations
      • Determining whether users can create accounts
      • Creating a new account in an existing organization
      • Single sign-on (SSO)
        • Structural user authentication with SSO
        • Enabling and configuring SSO on Structural Cloud
        • Synchronizing SSO groups with Structural
        • Viewing the list of SSO groups in Tonic Structural
        • AWS IAM Identity Center
        • Duo
        • GitHub
        • Google
        • Keycloak
        • Microsoft Entra ID (previously Azure Active Directory)
        • Okta
        • OpenID Connect (OIDC)
        • SAML
      • Managing Structural users
      • Managing permissions
        • About permission sets
        • Built-in permission sets
        • Available permissions
        • Viewing the lists of global and workspace permission sets
        • Configuring custom permission sets
        • Selecting default permission sets
        • Configuring access to global permission sets
        • Setting initial access to all global permissions
        • Granting Account Admin access for a Structural Cloud organization
    • Structural monitoring and logging
      • Monitoring Structural services
      • Performing health checks
      • Downloading the usage report
      • Tracking user access and permissions
      • Redacted and diagnostic (unredacted) logs
      • Data that Tonic.ai collects
      • Verifying and enabling telemetry sharing
    • Configuring environment settings
    • Updating Structural
  • Connecting to your data
    • About data connectors
    • Overview for database administrators
    • Data connector summary
    • Amazon DynamoDB
      • System requirements and limitations for DynamoDB
      • Structural differences and limitations with DynamoDB
      • Before you create a DynamoDB workspace
      • Configuring DynamoDB workspace data connections
    • Amazon EMR
      • Structural process overview for Amazon EMR
      • System requirements for Amazon EMR
      • Structural differences and limitations with Amazon EMR
      • Before you create an Amazon EMR workspace
        • Creating IAM roles for Structural and Amazon EMR
        • Creating Athena workgroups
        • Configuration for cross-account setups
      • Configuring Amazon EMR workspace data connections
    • Amazon Redshift
      • Structural process overview for Amazon Redshift
      • Structural differences and limitations with Amazon Redshift
      • Before you create an Amazon Redshift workspace
        • Required AWS instance profile permissions for Amazon Redshift
        • Setting up the AWS Lambda role for Amazon Redshift
        • AWS KMS permissions for Amazon SQS message encryption
        • Amazon Redshift-specific Structural environment settings
        • Source and destination database permissions for Amazon Redshift
      • Configuring Amazon Redshift workspace data connections
    • Databricks
      • Structural process overview for Databricks
      • System requirements for Databricks
      • Structural differences and limitations with Databricks
      • Before you create a Databricks workspace
        • Granting access to storage
        • Setting up your Databricks cluster
        • Configuring the destination database schema creation
      • Configuring Databricks workspace data connections
    • Db2 for LUW
      • System requirements for Db2 for LUW
      • Structural differences and limitations with Db2 for LUW
      • Before you create a Db2 for LUW workspace
      • Configuring Db2 for LUW workspace data connections
    • File connector
      • Overview of the file connector process
      • Supported file and content types
      • Structural differences and limitations with the file connector
      • Before you create a file connector workspace
      • Configuring the file connector storage type and output options
      • Managing file groups in a file connector workspace
      • Downloading generated file connector files
    • Google BigQuery
      • Structural differences and limitations with Google BigQuery
      • Before you create a Google BigQuery workspace
      • Configuring Google BigQuery workspace data connections
      • Resolving schema changes for de-identified views
    • MongoDB
      • System requirements for MongoDB
      • Structural differences and limitations with MongoDB
      • Configuring MongoDB workspace data connections
      • Other MongoDB hints and tips
    • MySQL
      • System requirements for MySQL
      • Before you create a MySQL workspace
      • Configuring MySQL workspace data connections
    • Oracle
      • Known limitations for Oracle schema objects
      • System requirements for Oracle
      • Structural differences and limitations with Oracle
      • Before you create an Oracle workspace
      • Configuring Oracle workspace data connections
    • PostgreSQL
      • System requirements for PostgreSQL
      • Before you create a PostgreSQL workspace
      • Configuring PostgreSQL workspace data connections
    • Salesforce
      • System requirements for Salesforce
      • Structural differences and limitations with Salesforce
      • Before you create a Salesforce workspace
      • Configuring Salesforce workspace data connections
    • Snowflake on AWS
      • Structural process overviews for Snowflake on AWS
      • Structural differences and limitations with Snowflake on AWS
      • Before you create a Snowflake on AWS workspace
        • Required AWS instance profile permissions for Snowflake on AWS
        • Other configuration for Lambda processing
        • Source and destination database permissions for Snowflake on AWS
        • Configuring whether Structural creates the Snowflake on AWS destination database schema
      • Configuring Snowflake on AWS workspace data connections
    • Snowflake on Azure
      • Structural process overview for Snowflake on Azure
      • Structural differences and limitations with Snowflake on Azure
      • Before you create a Snowflake on Azure workspace
      • Configuring Snowflake on Azure workspace data connections
    • Spark SDK
      • Structural process overview for the Spark SDK
      • Structural differences and limitations with the Spark SDK
      • Configuring Spark SDK workspace data connections
      • Using Spark to run de-identification of the data
    • SQL Server
      • System requirements for SQL Server
      • Before you create a SQL Server workspace
      • Configuring SQL Server workspace data connections
    • Yugabyte
      • System requirements for Yugabyte
      • Structural differences and limitations with Yugabyte
      • Before you create a Yugabyte workspace
      • Configuring Yugabyte workspace data connections
      • Troubleshooting Yugabyte data generation issues
  • Using the Structural API
    • About the Structural API
    • Getting an API token
    • Getting the workspace ID
    • Using the Structural API to perform tasks
      • Configure environment settings
      • Manage generator presets
        • Retrieving the list of generator presets
        • Structure of a generator preset
        • Creating a custom generator preset
        • Updating an existing generator preset
        • Deleting a generator preset
      • Manage custom sensitivity rules
      • Create a workspace
      • Connect to source and destination data
      • Manage file groups in a file connector workspace
      • Assign table modes and filters to source database tables
      • Set column sensitivity
      • Assign generators to columns
        • Getting the generator IDs and available metadata
        • Updating generator configurations
        • Structure of a generator assignment
        • Generator API reference
          • Address (AddressGenerator)
          • Algebraic (AlgebraicGenerator)
          • Alphanumeric String Key (AlphaNumericPkGenerator)
          • Array Character Scramble (ArrayTextMaskGenerator)
          • Array JSON Mask (ArrayJsonMaskGenerator)
          • Array Regex Mask (ArrayRegexMaskGenerator)
          • ASCII Key (AsciiPkGenerator)
          • Business Name (BusinessNameGenerator)
          • Categorical (CategoricalGenerator)
          • Character Scramble (TextMaskGenerator)
          • Character Substitution (StringMaskGenerator)
          • Company Name (CompanyNameGenerator)
          • Conditional (ConditionalGenerator)
          • Constant (ConstantGenerator)
          • Continuous (GaussianGenerator)
          • Cross Table Sum (CrossTableAggregateGenerator)
          • CSV Mask (CsvMaskGenerator)
          • Custom Categorical (CustomCategoricalGenerator)
          • Date Truncation (DateTruncationGenerator)
          • Email (EmailGenerator)
          • Event Timestamps (EventGenerator)
          • File Name (FileNameGenerator)
          • Find and Replace (FindAndReplaceGenerator)
          • FNR (FnrGenerator)
          • Geo (GeoGenerator)
          • HIPAA Address (HipaaAddressGenerator)
          • Hostname (HostnameGenerator)
          • HStore Mask (HStoreMaskGenerator)
          • HTML Mask (HtmlMaskGenerator)
          • Integer Key (IntegerPkGenerator)
          • International Address (InternationalAddressGenerator)
          • IP Address (IPAddressGenerator)
          • JSON Mask (JsonMaskGenerator)
          • MAC Address (MACAddressGenerator)
          • Mongo ObjectId Key (ObjectIdPkGenerator)
          • Name (NameGenerator)
          • Noise Generator (NoiseGenerator)
          • Null (NullGenerator)
          • Numeric String Key (NumericStringPkGenerator)
          • Passthrough (PassthroughGenerator)
          • Phone (USPhoneNumberGenerator)
          • Random Boolean (RandomBooleanGenerator)
          • Random Double (RandomDoubleGenerator)
          • Random Hash (RandomStringGenerator)
          • Random Integer (RandomIntegerGenerator)
          • Random Timestamp (RandomTimestampGenerator)
          • Random UUID (UUIDGenerator)
          • Regex Mask (RegexMaskGenerator)
          • Sequential Integer (UniqueIntegerGenerator)
          • Shipping Container (ShippingContainerGenerator)
          • SIN (SINGenerator)
          • SSN (SsnGenerator)
          • Struct Mask (StructMaskGenerator)
          • Timestamp Shift (TimestampShiftGenerator)
          • Unique Email (UniqueEmailGenerator)
          • URL (UrlGenerator)
          • UUID Key (UuidPkGenerator)
          • XML Mask (XmlMaskGenerator)
      • Configure subsetting
      • Check for and resolve schema changes
      • Run data generation jobs
      • Schedule data generation jobs
    • Example script: Starting a data generation job
    • Example script: Polling for a job status and creating a Docker package
Powered by GitBook
On this page
  • About Privacy Hub
  • Viewing the count of detected sensitive columns that are not protected
  • Viewing the protection status for each column
  • At-Risk Columns
  • Protected Columns
  • Not Sensitive Columns
  • Viewing the protection status for each table
  • Information in the list
  • Filtering the list
  • Sorting the list
  • Managing columns from the table list
  • Viewing and configuring columns
  • Navigating through columns and viewing column details
  • Indicating whether a column is sensitive
  • Selecting and configuring a generator for the column
  • Displaying sample data for a column
  • Enabling Document View for file connector JSON columns
  • Commenting on a column
  • Downloading a preview Privacy Report
  • From workspace management view
  • From Privacy Hub
  • Running a new sensitivity scan on the data

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  1. Configuring data generation

Privacy Hub

Last updated 9 days ago

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About Privacy Hub

Privacy Hub tracks the current protection status of source data columns based on:

  • , either from the most recent sensitivity scan or from manual assignments

  • Assigned

  • Assigned

To display Privacy Hub, either:

  • On the workspace management view, in the workspace navigation bar, click Privacy Hub.

  • On Workspaces view, click the workspace name.

From Privacy Hub, you can:

  • Review and apply the recommended generators for all detected sensitive columns

  • View the current protection status of columns

  • Manually mark columns as sensitive or not sensitive

  • Configure protection for sensitive columns

  • Download a preview Privacy Report

  • Run a new sensitivity scan

You can also track the history of changes to column sensitivity and the assigned column generators. For more information, go to Tracking changes to workspaces, generator presets, and sensitivity rules.

Viewing the count of detected sensitive columns that are not protected

The sensitivity scan detects specific types of sensitive data.

If your workspace contains any columns that the sensitivity scan identified, and for which you have not either:

  • Assigned a generator

  • Marked as not sensitive

Then Tonic Structural displays a Sensitivity Recommendations banner that contains a count of those columns.

The count only includes sensitive columns that the sensitivity scan detects. If you manually mark a column as sensitive, it is not included in the list.

On the banner, the Review Recommendations option allows you to review the detected columns and the recommended generators for each detected sensitive data type.

You can then apply the recommended generators or ignore the recommendations. When you ignore a recommendation, you either:

  • Indicate to remove the generator recommendation for the column.

  • Indicate that the column data is not sensitive.

For more information, go to Reviewing and applying recommended generators.

Viewing the protection status for each column

The protection status panels at the top of Privacy Hub provide an overview of the current protection status of the columns in the source data.

Each panel displays:

  • The number of columns that are in that category.

  • The estimated percentage of columns that are in that category.

The column counts do not include columns that do not have data in the destination database. For example, if a table is assigned Truncate table mode, then Privacy Hub ignores the columns in that table.

The information on these panels updates automatically as you change whether columns are sensitive and assign generators to columns.

At-Risk Columns

The At-Risk Columns panel reflects columns that:

  • Are populated in the destination database.

  • Are marked as sensitive.

  • Have the generator set to Passthrough, which indicates that Structural does not perform any transformation on the data.

For each column, the At-Risk Columns panel also indicates the sensitivity confidence, from full confidence (completely red) to low confidence (a small percentage of red).

The goal is to have 0 at-risk columns.

Protected Columns

The Protected Columns panel reflects columns that:

  • Are populated in the destination database.

  • Are assigned a generator other than Passthrough.

It includes both sensitive and non-sensitive columns.

Note that a column is considered protected based solely on the assigned generator. Some more complex generators, such as JSON Mask or Conditional, allow you to apply different generators to specific portions of a value or based on a specific condition. However, the protection status does not reflect these sub-generators. An applied sub-generator could be Passthrough.

Not Sensitive Columns

The Not Sensitive Columns panel reflects columns that:

  • Are populated in the destination database.

  • Are marked as not sensitive.

  • Have the generator set to Passthrough.

Viewing the protection status for each table

The Database Tables list shows the protection status for each table in the source database. You can view the number of columns that have each protection status, and update the column configuration.

The list does not include tables where the table mode is Truncate or Preserve Destination. Truncated tables are not populated in the destination database. For Preserve Destination tables, the existing data in the destination database does not change.

Information in the list

For each table, Database Tables provides the following information:

  • Privacy Status - Indicates the current protection status of the columns in the table. It provides the same view and configuration options as the protection status panels at the top of Privacy Hub.

Filtering the list

You can filter the Database Tables list either by the table name or by the schema.

Filtering by table name

To filter the list by table name, in the filter field, begin to type text that is in the table name. As you type, Structural updates the list to only display matching tables.

Filtering by schema

To filter the list to only include tables that belong to a specific schema:

  1. Click Filter by Schema.

  2. From the schema dropdown list, select the schema.

When you select a schema, Structural adds it to the filter field.

Sorting the list

You can sort the Database Tables list by any column except for the Privacy Status column.

To sort by a column, click the column heading. To reverse the sort order, click the heading again.

Managing columns from the table list

The Privacy Status column in the Database Tables list indicates the protection status of the columns in the table.

Viewing and configuring columns

Navigating through columns and viewing column details

Each protection status panel displays a series of boxes to represent the columns that apply to that status. For example, if the source data contains four columns that are at-risk, then the At-Risk Columns panel displays four boxes, one for each column.

The Privacy Status column in the Database Tables list displays the same set of boxes for the columns in an individual table.

If the number of columns is too large to fit, then the last box shows the number of additional columns that apply. For example, if there are 15 columns that don't fit, then the last box is labeled +15.

When you hover over a box, the column name displays in a tooltip.

When you click a box, the details panel for that column displays.

When you click the box for remaining columns, the details panel for the first column in the remaining columns displays.

You can use the next and previous icons at the bottom right of the details panel to display the details for the next or previous column.

The column details panel opens to the settings view. The settings view contains the following information:

  • The table and column name.

  • Whether the column is flagged as sensitive.

  • The type of sensitive data that the column contains.

  • The data type for the column data.

  • The generator that is assigned to the column.

  • For a child workspace, whether the column configuration is inherited from the parent workspace. For columns that have overrides, you can reset to the parent configuration.

Indicating whether a column is sensitive

Required workspace permission: Configure column sensitivity

From the settings view of the column details, you can configure the column sensitivity.

You cannot change the sensitivity of columns in a child workspace. A child workspace always inherits the sensitivity from its parent workspace. For more information, go to About workspace inheritance.

As you change the column sensitivity, Structural updates the protection status panels.

To change whether the column is sensitive, toggle the Sensitive option. The column is moved if needed to reflect its new status. However, you remain on the current panel.

For example, from the At-Risk Columns panel, you change a column to be not sensitive. The column is moved to the Not Sensitive Columns panel. When you click the next or previous icons, you view the details for the next or previous column on the At-Risk Columns panel.

Selecting and configuring a generator for the column

Required workspace permission: Configure column generators

From the column details, you can assign and configure the column generator.

When you change the column generator, Structural updates the protection status panels.

If the column generator was previously Passthrough, then the column is moved to the Protected Columns panel. However, you remain on the current panel. For example, you assign a generator to a column that is on the At-Risk Columns panel. The column is moved to the Protected Columns panel, but when you click the next or previous icons, you view the details for the next or previous column on the At-Risk Columns panel.

Selecting the generator

For sensitive columns that are not protected, Structural displays the recommended generator as a button.

For self-hosted instances that have an Enterprise license, the recommended generator is the built-in generator preset.

To assign the recommended generator to the column, click the button.

Otherwise, select the generator from the Generator Type dropdown list.

For more information about selecting a generator, go to Assigning and configuring generators.

Configuring the generator

If the selected generator requires additional configuration, then below the Generator Type dropdown list is an Edit Generator Options link.

To display the configuration fields for the generator, click Edit Generator Options.

For information about configuring a selected generator or generator preset, go to Assigning and configuring generators.

After you configure the generator, to return to the settings view, click Back.

Displaying sample data for a column

Required workspace permission:

  • Source data: Preview source data

  • Destination data: Preview destination data

From the column details, you can display sample data for the column. The sample data allows you to compare the source and destination versions of the column values.

To display the sample data, click the view sample (magnifying glass) icon.

On the sample data view of the column details:

  • The Original Data tab shows the values in the source data.

  • The Protected Output tab shows the values that the generator produced.

Enabling Document View for file connector JSON columns

For a file connector JSON column, instead of assigning a generator, you can enable Document View.

From Document View, you can view the JSON schema structure and assign generators to individual JSON fields. For more information, go to Document View for file connector JSON columns.

To enable Document View, on the column details panel, toggle Use Document View to the on position. When Document View is enabled, the generator dropdown is replaced with the Open in Document View option.

Commenting on a column

Required license: Professional or Enterprise

From the column details, you can view and add comments on the column. You might use a comment to explain why you selected a particular generator or marked a column as sensitive or not sensitive.

From the column details, to display the comments for the column, click the comment icon.

The comments view displays any existing comments on the column. The most recent comment is at the bottom of the list. Each comment includes the name of the user who made the comment.

To add the first comment to a column, type the comment into the comment text area, then click Comment.

To add an additional comment, type the comment into the comment text area, then click Reply.

Downloading a preview Privacy Report

Required license: Enterprise

The Privacy Report files that you download from Privacy Hub or the workspace download menu provide an overview of the current protection status based on the current configuration.

This is different from the Privacy Report files that you download from the data generation job details, which show the protection status for the data produced by that data generation.

You can download either:

  • The Privacy Report .csv file, which provides details about the table columns, the column content, and the current protection configuration.

  • The Privacy Report PDF file, which provides charts that summarize the privacy ranking scores for the table columns. It also includes the table from the .csv file.

For more information about the Privacy Report files and their content, go to Using the Privacy Report to verify data protection.

From workspace management view

To download the report from the workspace management view, click the download icon. In the download menu:

  • To download the Privacy Report PDF file, click Download Privacy Report PDF.

  • To download the Privacy Report .csv file, click Download Privacy Report CSV.

From Privacy Hub

To download the report from Privacy Hub, click Reports and Logs, then:

  • To download the Privacy Report .csv file, click Privacy Report CSV.

  • To download the Privacy Report PDF file, click Privacy Report PDF.

Running a new sensitivity scan on the data

Required workspace permission: Run sensitivity scan

  • You add columns to the source database. The new scan identifies whether the new columns contain sensitive data.

  • The data in a column changes significantly, and a column that Structural originally marked as not sensitive might now contain sensitive data.

To run a new sensitivity scan, click Run Sensitivity Scan.

When Structural runs a new sensitivity scan:

  • Structural analyzes and determines the sensitivity of any new columns.

  • It does not change the sensitivity of existing columns that you marked as sensitive or not sensitive.

  • For existing columns that you did not change the sensitivity of:

    • Structural does not change the sensitivity of columns that the original scan marked as sensitive.

    • It can change the sensitivity of columns that the original scan marked as not sensitive.

The protection status panels are updated to reflect the results of the new scan.

Note that for a , the protection status displays a separate box for each combination of JSON path and data type.

From each panel, you can .

When you click Open in Database View, you navigate to . The column list is filtered to show columns that are at risk.

When you click Open in Database View, you navigate to . The column list is filtered to show all included columns that are protected.

When you click Open in Database View, you navigate to . The column list is filtered to show included columns that are not sensitive and are not protected.

Name - The table name. For a workspace, each table corresponds to a file group. Each is also in a separate row. For JSON columns, the Name column displays both the table name and the column name.

Not Sensitive - The number of not sensitive columns in the table. Not sensitive columns are not marked as sensitive and have Passthrough as the generator. When you click the value, you navigate to , filtered to display the not sensitive columns for the table.

Protected - The number of protected columns in the table. Protected columns have an assigned generator. A protected column can be either sensitive or not sensitive. When you click the value, you navigate to , filtered to display the protected columns for the table.

At-Risk - The number of at-risk columns in the table. These columns are marked as sensitive, but have Passthrough as the generator. The goal is to have 0 unprotected sensitive columns. When you click the value, you navigate to , filtered to display the at-risk columns for the table.

This column provides the same as the protection status panels at the top of Privacy Hub, but is limited to the columns in a specific table.

Privacy Hub provides an option to manually start a new . For example, you might want to run a new sensitivity scan when:

You cannot run a sensitivity scan on a . Child workspaces always inherit the sensitivity results from their parent workspace.

JSON column that uses Document View
Database View
Database View
Database View
file connector
JSON column that uses Document View
Database View
Database View
Database View
sensitivity scan
child workspace
display details for and configure protection for each column
options to view and configure columns
Column sensitivity
table modes
generators
Privacy Hub
Sensitivity Recommendations banner on Privacy Hub
Protection status panels
Settings view of column details panel
Column details panel with generator selected
Configuration options for a selected generator
Sample data view on the column details panel
Comment view of the column details panel
Download menu for a workspace
Reports and Logs menu on Privacy Hub
Buttons at the top of Privacy Hub