How to Mask Data in BigQuery

How do I mask sensitive columns in BigQuery without duplicating tables?

Data masking in BigQuery hides or obfuscates sensitive column values at query time so unauthorized users see anonymized or null results.

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Description

Example H2

Example H3

Why mask data in BigQuery?

Prevent accidental exposure of PII while letting analysts work with useful datasets. Masked data supports compliance with GDPR, HIPAA, and SOC2 without duplicating tables.

What masking options exist?

You can ➊ apply dynamic data masking policies using policy tags, ➋ build authorized views that transform columns, or ➌ use masking functions (e.g., SHA256, REGEXP_REPLACE) directly in queries.

How do policy-tag masks work?

Create a policy tag, attach it to a column, and define a masking rule such as NULL, HASH, or PARTIAL. Users lacking the bigquery.policyTags.get permission for that tag will see the masked value automatically.

Steps to set up a masking policy tag

1. Enable BigQuery Data Catalog API.
2. CREATE POLICY TAG.
3. ALTER TABLE ... ALTER COLUMN ... SET POLICY TAG.
4. Grant Data Catalog Fine-grained Reader to roles who should see clear text.

How to partially mask customer emails?

Use REGEXP_REPLACE to reveal the first letter and domain while hiding the rest. This keeps data useful for deduping but protects identities.

How to hash customer IDs for joins?

SHA256 or FARM_FINGERPRINT turns IDs into irreversible hashes. Downstream teams can still join on the hash but cannot retrieve the original ID.

When should I choose authorized views?

Authorized views fit when you need complex masking logic or cross-table joins but want to expose a single secure interface. Grant users access to the view, not the base tables.

Best practices for BigQuery masking

Document policy tags in your data catalog, version-control masking SQL, and test with least-privilege accounts. Combine masking with row-level security for maximum protection.

Why How to Mask Data in BigQuery is important

How to Mask Data in BigQuery Example Usage


--Show masked emails for analysts without the policy-tag permission
SELECT id,
       email AS masked_email,
       created_at
FROM `ecom.Customers`
WHERE created_at >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY);

How to Mask Data in BigQuery Syntax


--1. Create a policy tag with a NULL mask
CREATE POLICY TAG `galaxy_masking.pii_email_mask`
WITH DATA MASKING POLICY NULL;

--2. Attach the tag to the Customers.email column
ALTER TABLE `ecom.Customers`
ALTER COLUMN email
SET OPTIONS (policy_tags=['projects/123456/locations/us/taxonomies/789/policyTags/456']);

--3. Partial mask inside a view
CREATE OR REPLACE VIEW ecom.secure_customers AS
SELECT id,
       REGEXP_REPLACE(email, r'(^.).+(@.+$)', '\\1****\\2') AS email,
       name,
       created_at
FROM ecom.Customers;

--4. Hash IDs for joins across datasets
SELECT SHA256(CAST(id AS STRING)) AS hashed_id, name
FROM ecom.Customers;

Common Mistakes

Attaching a masking policy tag but forgetting to revoke original table access. Users still holding the BigQuery Data Viewer role on the dataset will bypass the mask. Fix by limiting permissions to the authorized view or table with the tag.
Using reversible functions like BASE64 or simple ROT13 for hashing. Attackers can decode them easily. Use one-way hashes such as SHA256 or BigQuery's FARM_FINGERPRINT.

Frequently Asked Questions (FAQs)

Can I combine row-level security with data masking?

Yes. Apply a row-level security policy to filter records and a policy tag to mask columns. Both rules execute before results are returned.

Does masking slow down queries?

Policy-tag masking is applied by the storage engine and has negligible impact. SQL functions inside views add minimal overhead, usually sub-millisecond per row.

Can service accounts bypass masking?

Only if they hold Data Catalog Fine-grained Reader or BigQuery Admin roles on the specific policy tag’s taxonomy. Restrict those roles to trusted principals.