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AI agents offer flexible data access control to balance insights with privacy and security. By default, agents work with metadata only, but you can optionally enable data access for deeper analysis.

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Data access modes

Metadata-only mode (default):
  • Agents can see your data structure, field names, and model definitions
  • They can generate appropriate queries and visualizations
  • No actual data values (except for one-row query results) are shared with the agent
  • Perfect for exploring data structure and creating initial analyses
Data access enabled:
  • Agents receive actual query results in addition to metadata
  • Can provide specific insights, identify trends, and analyze patterns in your data
  • Offers detailed summaries and data-driven recommendations
  • Can search for actual field values to ensure accurate filters when building visualizations
  • Only shares data when explicitly enabled per agent
Data access is optional and controlled per agent. When disabled, agents only work with your data model structure and cannot see actual data values. This ensures sensitive information is only shared when you explicitly choose to enable this capability.
To enable data access, go to your agent settings and toggle the “Data Access” option.
How to enable data access for AI agents

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User attributes and permissions

AI agents automatically respect all data access controls configured through user attributes. This ensures that agents only access data that the user is authorized to see, maintaining your existing security policies.

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How user attributes flow through AI queries

When an AI agent generates and executes queries on behalf of a user, it inherits that user’s attribute values and applies them to all data access: Row-level security:
  • Agents automatically apply sql_filter rules defined in your models
  • Only rows matching the user’s attribute values are included in query results
  • Example: If a user has sales_region: 'EMEA', the agent will only query data for that region
Column-level security:
  • Agents respect required_attributes on dimensions
  • Columns the user cannot access are invisible to the agent
  • Metrics derived from restricted columns are also unavailable
  • Example: If salary requires is_admin: 'true', non-admin users’ agents cannot query salary data
Table-level security:
  • Agents respect required_attributes on models
  • Tables the user cannot access are completely hidden from the agent
  • The agent cannot reference or join restricted tables
  • Example: If payments requires is_admin: 'true', non-admin users’ agents cannot query the payments table

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Default behavior

In the Lightdash app: AI agents automatically use the logged-in user’s attributes for all queries.In Slack: AI agents currently use the attributes of the user who created the agent. We plan to respect user attributes based on Slack user email in the future—reach out if you need this feature!

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How this works behind the scenes

When an agent generates a query:
  1. The agent receives the user’s complete attribute profile (both direct user attributes and group attributes)
  2. All sql_filter rules are automatically applied to the generated SQL
  3. Dimensions and tables with required_attributes are filtered from the available schema
  4. The agent only sees and can query data within the user’s permissions
This happens transparently—the agent doesn’t need special configuration. Your existing user attribute rules automatically protect AI-generated queries just like they protect manual queries.

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Example: Regional sales access

Consider this model configuration: 
models:
  - name: sales
    meta:
      sql_filter: ${TABLE}.region IN (${lightdash.attributes.sales_region})
    columns:
      - name: revenue
      - name: customer_name
        meta:
          dimension:
            required_attributes:
              can_view_pii: "true"
For a user with sales_region: 'EMEA' and no can_view_pii attribute:
  • The agent can query revenue data, but only for EMEA region
  • The agent cannot see or query customer_name (PII restriction)
  • If the agent tries to analyze customer names, it will fail with a permissions error
  • All generated queries automatically include WHERE region IN ('EMEA')

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Security considerations

  • Metadata mode: Even in metadata-only mode, agents respect user attributes when showing available fields
  • Data access mode: When data access is enabled, query results are filtered by user attributes
  • Query generation: Agents cannot generate queries that bypass user attribute restrictions
  • Error handling: If an agent attempts to access restricted data, the query fails with a permissions error
Custom SQL in table calculations or SQL Runner can potentially bypass user attribute filters. AI agents use the standard query interface and cannot bypass these restrictions.