Introduction to BigQuery row-level security

This document explains the concept of row-level security, how it works in BigQuery, when to use row-level security to secure your data, and other details.

What is row-level security?

Row-level security lets you filter data and enables access to specific rows in a table based on qualifying user conditions.

BigQuery already supports access controls at the project, dataset, and table levels, as well as column-level security through policy tags. Row-level security extends the principle of least privilege by enabling fine-grained access control to a subset of data in a BigQuery table, by means of row-level access policies.

One table can have multiple row-level access policies. Row-level access policies can coexist on a table with column-level security as well as table-level, dataset-level, and project-level access controls.

How row-level security works

At a high level, row-level security involves the creation of row-level access policies on a target BigQuery table. These policies act as filters to hide or display certain rows of data, depending on whether a user or group is in an allowed list.

An authorized user, with the Identity and Access Management (IAM) roles BigQuery Admin or BigQuery DataOwner, can create row-level access policies on a BigQuery table.

When you create a row-level access policy, you specify the table by name, and which users or groups (called the grantee-list) can access certain row data. The policy also includes the data on which you want to filter, called the filter_expression. The filter_expression functions like a WHERE clause in a typical query.

For instructions on how to create and use a row-level access policy, see Working with row-level security.

See the DDL reference for the complete syntax, usage, and options when creating row-level access policies.

Example use cases

Filter row data based on region

Consider the case where a table dataset1.table1 contains rows belonging to different regions (denoted by the region column).

Row-level security lets a Data Owner or Admin implement policies, such as "Users in the group:apac can only see partners from the APAC region."

Row-level security use case for regions

The resulting behavior is that users in the group can view only rows where Region = "APAC". Similarly, users in the group can view only rows in the US region. Users not in APAC or US groups don't see any rows.

The row-level access policy named us_filter grants access to multiple entities, including the chief US salesperson, all of whom can now access the rows belonging to the US region.

Filter row data based on sensitive data

Now, consider a different use case, where we have a table of salary data.

Row-level security use case for salaries

The grantee_list restricts querying to members of the company domain. In addition, the use of the SESSION_USER() function further restricts access only to rows that belong to the user running the query, based on their own user email address. In this case, it is

When to use row-level security vs other methods

Authorized views, row-level access policies, and storing data in separate tables all provide different levels of security, performance, and convenience. Choosing the right mechanism for your use case is important to ensure the proper level of security for your data.

Comparison with authorized views: vulnerabilities

Both row-level security and enforcing row-level access with an authorized view can have vulnerabilities, if used improperly.

When you use either authorized views or row-level access policies for row-level security, we recommend that you monitor for any suspicious activity using audit logging.

Side channels, such as the query duration, can leak information about rows that are at the edge of a storage shard. Such attacks would likely require either some knowledge of how the table is sharded, or a large number of queries.

For more information about preventing such side-channel attacks, see Best practices for row-level security.

Comparison of authorized views, row-level security, and separate tables

The following table compares the performance and security of authorized views, row-level access policies, and separate tables.

Security Recommended for
Vulnerable to carefully crafted queries, query duration, and other types of side-channel attack. When flexibility and performance are most important.

Example: sharing data within the same work group.
Row-level access policies Vulnerable to carefully crafted queries and query duration side-channel attacks. When it is convenient to have all users query the same table. For instance, when everyone shares the same dashboard, but some users have access to more data.

To provide additional security over views.

Example: sharing table slices within your organization.
Separate tables Complete isolation. When isolation is paramount. For instance, when the total number of rows must be secret.

Example: sharing data outside your organization, such as with third-party partners and vendors.

Create and manage row-level access policies

For information about how to create, update (re-create), list, view, and delete row-level access policies on a table, and how to query tables with row-level access policies, see Working with row-level access security.


For more information about quotas and limits for row-level security, see BigQuery Quotas and limits.


Row-level security is included with BigQuery at no cost.

Billing costs for accessing a table's row-level access policy are similar to a query. However, row-level access policies might indirectly affect the number of bytes processed, in the following ways.

  • When a query is run against a table with a row-level access policy, the bytes billed is calculated in the same way as if you had composed an identical query with a WHERE clause, instead of the filter expression.
  • Row-level access policy filters do not participate in query pruning on partitioned and clustered tables.

For more information about BigQuery query pricing, see BigQuery pricing.


For information about limits for row-level security, see BigQuery Row-level security limits. The following sections document additional row-level security limitations.

Performance limitations

For more information about how row-level security interacts with some BigQuery features and services, see Using row-level security with other BigQuery features.

Security limitations

For more information about limiting side-channel attacks, see Best practices for row-level security in BigQuery.

Other limitations

  • A table can have up to 100 row access policies.

  • Row access policies are not compatible with Legacy SQL. Queries of tables with row-level access policies must use Standard SQL. Legacy SQL queries are rejected with an error.

  • You cannot apply row-level access policies on JSON columns.

  • Some features of BigQuery are not compatible with row-level security. For more information, see Using row-level security.

  • Non-query operations, including service account jobs, that need full access to table data can use row-level security with the TRUE filter. Examples include table copying, dataproc workflows, and more. For more information, see Using row-level security.

  • Creating, replacing, or deleting row-level access policies must be performed with DDL statements. Listing and viewing row-level access policies can be performed through the Google Cloud console or the bq command-line tool.

  • Table sampling is not compatible with row-level security.

Audit logging and monitoring

When data in a table with one or more row-level access policies is read, the row-level access policies authorized for the read access appear in the IAM authorization information for that read request.

Creation and deletion of row-level access policies are audit logged, and can be accessed through Cloud Logging. Audit logs include the name of the row-level access policy. However, the filter_expression and grantee_list definitions of a row-level access policy are omitted from logs, as they may contain user or other sensitive information. Listing and viewing of row-level access policies are not audit logged.

For more information about logging in BigQuery, see Introduction to BigQuery monitoring.

For more information about logging in Google Cloud, see Cloud Logging.

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