user-scoped-data-filtering

name

user-scoped-data-filtering

description

Provides patterns and guidance for implementing user-scoped data filtering and multi-tenancy in web applications. Use this skill when you need to: (1) Restrict data access based on user identity, (2) Implement ownership checks for database operations, (3) Build multi-tenant applications with organization-level data scoping, (4) Implement admin bypass for viewing all data, (5) Create audit trails for data access. This skill focuses on Python, FastAPI, and SQLAlchemy.

User-Scoped Data Filtering

This skill provides patterns and best practices for implementing robust user-scoped data filtering and multi-tenancy in your application. The focus is on a Python-based stack using FastAPI for the web framework and SQLAlchemy for the ORM.

Overview

Implementing proper data scoping is critical for security and privacy in multi-user applications. This skill breaks down the process into several key areas, each with its own reference guide.

Core Concepts & Workflow

1. User Context Extraction: The first step is to reliably identify the current user. This is typically done by extracting user information from an authentication token (e.g., JWT).

   • For implementation details, see references/user-context.md.

2. Scoped Database Queries: Once you have the user's identity, you must filter all database queries to only return data they are authorized to see.

   • For service layer patterns and SQLAlchemy examples, see references/scoped-queries.md.

3. Ownership Verification: For write operations (updates and deletes), you must verify that the user owns the resource they are trying to modify. This is often implemented using decorators or dependencies.

   • For decorator patterns, see references/ownership-verification.md.

4. Multi-Tenancy: In many applications, data is scoped not just by user, but by a higher-level entity like an organization or a team.

   • For examples of organization-level scoping, see references/multi-tenancy.md.

5. Admin Bypass: Administrative users often need to bypass these scoping rules to view and manage data across all users and organizations.

   • For patterns to implement this safely, see references/admin-bypass.md.

6. Audit Trail: It's important to log who is accessing and modifying data, especially when admin bypass is used.

   • For guidance on creating audit trails, see references/audit-trail.md.

Getting Started

Start by reviewing references/user-context.md to set up your user identification dependency. Then, move on to references/scoped-queries.md to apply filtering to your data access layer. Implement the other patterns as needed for your application's requirements.