• 29 Oct 2026

How to Implement Row-Level Security in Amazon QuickSight

Quick Summary

What Is Row-Level Security in Amazon QuickSight?

Row-Level Security (RLS) in Amazon QuickSight is a data access control feature that restricts the rows of a dataset visible to individual users or groups.

Instead of creating separate datasets and dashboards for every department, region, or customer, organizations can apply security rules to a shared dataset. These rules determine which records users can see.

For example, a healthcare organization may use a shared dashboard to analyze operational performance across multiple facilities. With RLS, a regional manager can access data for their assigned facilities without automatically receiving access to every other region's records.

Similarly, a financial services organization can restrict sales dashboards so that employees see only the records authorized for their roles.

Why is RLS important?

Organizations often need to share analytics while maintaining appropriate access boundaries. RLS helps them:

• Restrict access to records based on users, groups, or configured session attributes.
• Support shared dashboards across departments and business units.
• Reduce the need to maintain separate datasets for every audience.
• Protect sensitive business information through dataset-level access rules.
• Support data governance and access management requirements.

RLS is one component of a broader security strategy. Combine it with appropriate identity management, dataset permissions, access controls, and testing.

How Does Row-Level Security Work in QuickSight?

Amazon QuickSight applies RLS to datasets using configured rules. Analyses and dashboards that use the secured dataset inherit the applicable row-level restrictions.

The rules determine which data values a user or group is authorized to access.

A typical user-based RLS implementation involves three main components:

1. Main dataset: Contains the business records displayed in the analysis or dashboard.
2. Permissions dataset: Maps authorized users or groups to the values they are allowed to see.
3. RLS configuration: Connects the permissions rules to the main dataset so QuickSight can enforce the restrictions.

For example, suppose a sales dataset contains records for the East, West, and North regions. A permissions dataset can map different users to their permitted regions.

When an authorized user accesses a dashboard, the configured rules restrict the rows available to that user.

Example of how RLS works

This example illustrates the principle of mapping users to data. The actual configuration must follow QuickSight's supported RLS rule format, identity requirements, and matching behavior.

For technical details, consult the official AWS documentation on user-based RLS rules.

Types of Row-Level Security in QuickSight

QuickSight supports different approaches to row-level access control. The right method depends on how users access dashboards and how you manage permissions.

User-based rules

User-based rules associate registered QuickSight users or groups with permitted values in a dataset.

For example, a sales manager may be allowed to access records for two regions, while another manager can access records for only one region.

User-based rules are useful when access permissions are tied to registered identities and organizational roles.

Tag-based rules

Tag-based RLS uses session tags to control access in supported embedded analytics scenarios, including anonymous embedding configurations.

Instead of relying exclusively on a registered user's identity, the application supplies configured session attributes that determine which records are visible.

This approach can be useful when an application embeds dashboards for customers or other users who do not access QuickSight directly as registered users.

Which approach should you choose?

The correct approach depends on the identity model, embedding method, and security requirements. Verify the applicable QuickSight edition and feature prerequisites before implementation.

Prerequisites for Implementing QuickSight RLS

Before configuring row-level security, review the following requirements.

Prepare the main dataset

Identify the field or fields that determine which records a user may access. Examples include region, customer identifier, facility identifier, department, or business unit.

Ensure the relevant fields contain consistent values and can be matched to the permission rules.

Identify users and groups

Determine how permissions will be assigned and maintained. Confirm the identities or group identifiers required by your selected RLS approach.

Prepare the permissions dataset

Create a permissions dataset with the required identity fields and the corresponding values users are authorized to access.

Check for duplicate, missing, or inconsistent mappings before applying the rules.

Verify permissions and access controls

Confirm that administrators have the appropriate permissions to configure the dataset and that intended users have the necessary access to the relevant QuickSight resources.

Define a testing plan

Identify representative users, including users with different access levels. Establish expected results for authorized and unauthorized records before deploying the rules.

How to Implement Row-Level Security in Amazon QuickSight

The following workflow outlines the main implementation stages. Exact interface labels and configuration options may vary by QuickSight edition and product updates.

Step 1: Identify the data that needs protection

Open the dataset that supports your QuickSight analysis or dashboard.

Identify the field or fields that determine access restrictions.

For example, a sales dataset might contain:

• Transaction ID
• Salesperson
• Region
• Product
• Revenue

If users should see only their assigned regions, the Region field may be part of the RLS mapping.

Choose fields that represent the intended security boundary. Do not assume that hiding a field or applying a visual-level filter provides the same protection as dataset-level RLS.

Step 2: Create the permissions dataset

Prepare a separate dataset that maps users or groups to permitted values in the main dataset.

For example:

This is an illustrative example, not a complete production-ready permissions file. Use the identity and rule fields required by the selected QuickSight RLS configuration.

Validate the usernames, group identifiers, and values before proceeding.

Step 3: Load and validate the permissions data

Make the permissions data available through a supported source and load it into QuickSight.

Check that:

• Required identity fields are present.
• Field names follow the required format.
• Values match the corresponding fields in the main dataset.
• Unintended duplicate or blank mappings have been identified.
• The permissions dataset follows QuickSight's applicable rule limits and requirements.

A mismatch between a permissions value and the main dataset can prevent the expected rows from being returned.

Step 4: Configure row-level security

Open the relevant dataset's security or RLS configuration.

Select the supported rule method and configure the permissions dataset or rules as required.

For user-based rules, map the relevant identity field to the corresponding user or group identifier and associate it with the field values that determine data access.

For tag-based rules, configure the applicable session-tag mappings for the supported embedding scenario.

Follow the current AWS QuickSight RLS documentation for the exact configuration steps.

Step 5: Apply and validate the rules

After configuring RLS, validate the behavior using representative users and datasets.

Check that:

• Each user sees the records permitted by their rules.
• Users cannot access records assigned exclusively to other users or groups.
• Users with multiple authorized values receive the intended combined access.
• Missing or invalid mappings do not expose unintended records.
• The dashboard and its underlying dataset behave as expected.

Do not assume that a successful configuration automatically proves that all access paths are secure.

Step 6: Test before production deployment

Test the implementation in a controlled environment before releasing it to business users.

Include scenarios for authorized access, unauthorized access, users with multiple permissions, missing mappings, and changes to permissions.

Document the expected and observed results. Resolve unexpected access before enabling the configuration for production use.

Example: Creating a QuickSight Permissions Dataset

Consider an organization that uses a shared sales dashboard across three regions.

The main dataset contains sales records for East, West, and North. The organization wants each analyst to see only their assigned region.

Sample main dataset

Sample permissions dataset

Expected outcome

• analyst1@example.com can access the East records covered by the rules.
• analyst2@example.com can access the West records covered by the rules.
• analyst3@example.com can access the North records covered by the rules.

If a user is authorized for multiple regions, the permissions dataset may need multiple corresponding rule records, depending on the configured rule model.

This example demonstrates the basic concept. Production implementations must use the exact field names, supported identity fields, and rule format required by QuickSight.

What should you validate?

Check that every permitted region exists in the main dataset and that the identity values match the appropriate QuickSight users or groups.

Test the configuration with each representative user and confirm that unauthorized rows remain inaccessible. Also validate behavior when users are added, removed, or assigned different regions.

QuickSight RLS Data-Type Limitations

A common implementation consideration is the data type used in RLS rules.

AWS documentation states that fields used for standard user-based RLS rule matching must contain text, such as string, character, or varchar values. Numeric and date fields are not directly supported as rule-matching fields in the same way.

What does this mean?

Suppose a dataset uses a numeric field called CustomerID to identify customer records. If that field is used in a permissions rule, you may need to create an appropriate string representation for matching, subject to the supported configuration.

Similarly, a date-based restriction may require a carefully designed textual representation if the relevant RLS configuration supports that mapping.

However, converting values to strings does not automatically make every security scenario valid. The representation must be consistent, unambiguous, and correctly matched to the underlying dataset.

Recommended practices

• Confirm the supported field types in the current AWS documentation.
• Use consistent formatting for identifiers and dates.
• Preserve leading zeros where they are meaningful.
• Validate that converted values match the main dataset.
• Test boundary cases and missing values.
• Review the implications of any calculated or transformed fields used in the security model.

For the latest technical requirements, consult AWS documentation for user-based row-level security.

Common QuickSight RLS Issues and Troubleshooting

Even when RLS is configured correctly, inconsistent identities, permission mappings, or field values can produce unexpected results.

How to troubleshoot effectively

Start by identifying one affected user and a small set of records that should or should not be visible.

Trace the user's identity through the permissions dataset, verify the relevant field values, and compare the expected access with the observed result.

Test changes in a controlled environment before modifying production security rules. Avoid broadening access as a temporary fix without understanding the cause.

QuickSight RLS Best Practices

A reliable RLS implementation requires consistent permissions management, testing, and ongoing monitoring.

Follow the principle of least privilege

Grant only the data access necessary for each user or group. Avoid broad or blank permissions mappings unless the documented behavior is explicitly intended and appropriately restricted.

Maintain permissions data carefully

Establish a process for updating access when employees change roles, join a department, or leave the organization.

Review stale mappings and ensure that changes to permissions are reflected in the appropriate dataset and configuration.

Test positive and negative scenarios

Verify both the records users should see and the records they must not see.

Include users with multiple permissions, missing mappings, and recently updated access.

Monitor data quality

Keep identity fields, rule values, and relevant dataset fields consistent. Document data transformations that affect the values used by RLS.

Protect the broader analytics environment

RLS is not a replacement for appropriate identity management, dataset permissions, secure embedding, and other access controls.

Review the complete access path to ensure that sensitive data is protected consistently.

Document and review the configuration

Maintain records of the permissions model, rule ownership, testing outcomes, and changes. Periodically review whether the configuration still matches business requirements.

Key Takeaways

• Row-level security restricts dataset rows based on configured access permissions.
• QuickSight supports user-based rules and tag-based approaches for applicable use cases.
• A permissions dataset can map users or groups to authorized data values.
• Correct identity mappings and consistent field values are essential for expected results.
• Standard user-based RLS rule matching requires attention to supported textual data types.
• Test both authorized and unauthorized access before production deployment.
• RLS should be combined with broader identity, dataset, and application security controls.

Conclusion

Row-Level Security in Amazon QuickSight enables organizations to share dashboards and analytics while controlling which records users can access. When implemented correctly, it can support regional reporting, departmental analytics, customer-specific dashboards, and other scenarios that require differentiated data access.

A successful implementation requires more than enabling a setting. Organizations should prepare a reliable permissions dataset, configure the appropriate RLS method, validate identity mappings, understand data-type limitations, and test access across representative scenarios.

By combining clear access rules with ongoing permissions reviews and security testing, organizations can build more reliable analytics experiences while protecting sensitive business information.

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