As a SaaS or consumer app developer, securing your multi-tenant data application is one of your main tasks. Propel offers a simple, yet powerful, solution using Access Policies and JWT tokens.

​
Key concepts

  1. Access Policies: Rules that determine which data each tenant can access.
  2. JWT Tokens: Secure tokens that carry tenant-specific information.
  3. Dynamic row-level filtering: Allows one policy to serve multiple tenants.
Here’s how to implement multi-tenant access control for your app:

​
Implementing multi-tenant security

1

Create a single, dynamic access policy

Instead of creating a separate policy for each tenant, define one policy with dynamic filtering:
Creating an Access Policy for multi-tenant access control

Creating an Access Policy for multi-tenant access control

—This policy uses ${{ tenant_id }} as a placeholder, which will be filled with the actual tenant ID at runtime.
2

Generate tenant-specific JWT tokens

When a user logs in, mint a JWT token that includes their tenant ID:
curl https://auth.propeldata.com/oauth2/token \
  -d grant_type=client_credentials \
  -d client_id=$YOUR_APP_ID \
  -d client_secret=$YOUR_APP_SECRET \
  -d 'policy_values={"tenant_id":"123"}'
3

Use the token for data queries

When querying Propel’s API, include this token in the Authorization header. Propel will automatically apply the correct tenant filter.

​
Benefits

  • Scalability: One policy serves all tenants.
  • Security: Each tenant is strictly limited to their own data.
  • Flexibility: Easily adapt to changes in your data model.
  • Multiple levels of tenancy: This models supports multiple levels of tenancy (e.g. Organizations, customers, workspaces and users).

​
Important Notes

  • Always create tokens server-side for security.
  • The resulting token is safe to use in frontend code.
  • You can include multiple tenant-specific values in the token if needed.
By following this approach, you can efficiently secure your multi-tenant application, ensuring that each customer only accesses their own data while minimizing the overhead of policy management.