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Documentation Index

Fetch the complete documentation index at: https://docs.wisdom.ai/llms.txt

Use this file to discover all available pages before exploring further.

This tutorial demonstrates how to protect sensitive information in your datasets while still enabling high-level analysis on the unmasked columns within a row. You will learn how to configure Column-Level Security (CLS) to mask specific values based on user attributes.

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Configure CLS

Follow these steps to configure CLS:
  1. Navigate to the Data Sources tab in your project.
  2. Select the table you wish to modify (e.g., Account).
cls-data-source
  1. Ensure that Show advanced options is toggled on in the top-right corner.
  2. Locate your sensitive column and click Configure under the CLS Masking column.
cls-sensitive-column
  1. In the Column-Level Security modal, provide a SQL expression that determines how the data is displayed at query time.
cls-sql-expression
  1. Click Save. The status badge in the CLS Masking column will now show as Active.
cls-active
If you enter an incorrect value, such as a column name that doesn’t exist, the system will alert you to the error and will not let you save it.

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Verifying the Implementation

Once active, CLS functions as a global security layer. It automatically enforces your masking logic across WisdomAI Chat, Dashboards, the SQL Playground, and Data Source Previews.
cls-chat
For unauthorized users, protected data will appear as hashed values. It is important to remember that CLS is applied strictly at the column level.
Avoid Data Leakage: If you protect a column (e.g., Customer Name) but leave a correlative column unprotected (e.g., Customer Email), an unauthorized user will see the hashed name, but the clear-text email address. Ensure you apply masking to all columns that could potentially identify the sensitive entity.

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Next Steps

Users Management

Manage user roles, permissions, and access to the platform.

Basic Tutorial: Connect and Test

Walk through the initial setup to connect a data source and run your first query.