RLS policy enforcement

[ralyodio]

📋 Product Requirements Document

PRD: RLS policy enforcement

Issue: #141
Milestone: Phase 4: Security
Labels: authentication-authorization, hacktoberfest

---

PRD: RLS Policy Enforcement

Issue: #141
Milestone: Phase 4: Security
Labels: authentication-authorization, hacktoberfest
Phase: Phase 4
Section: Authentication & Authorization

---

Overview

Row-Level Security (RLS) policy enforcement is a critical feature for MeshHook to ensure data isolation and security across multi-tenant environments. This task focuses on implementing RLS policies that will dynamically restrict access to data at the database level based on the tenant (project) identity. Aligning with MeshHook's goals, RLS enforcement will bolster security, ensuring that webhook triggers, workflow executions, and logs are accessible only by authorized users within their respective projects.

Objective: Implement and enforce RLS policies to ensure data isolation across tenants.

Functional Requirements

1. RLS Implementation: Develop and apply RLS policies for all relevant tables and views in the MeshHook database, ensuring data isolation is enforced based on project_id.
2. Tenant Context: Automatically set the tenant context (project_id) for all database queries based on the authenticated user's session or API request metadata.
3. Policy Management: Provide mechanisms for defining, altering, and removing RLS policies as part of the administrative functions, with changes audited for compliance.
4. Integration Testing: Ensure that RLS policies are correctly enforced across different scenarios, including cross-tenant data access attempts and tenant data modifications.

Non-Functional Requirements

• Security: RLS policies must be robust, preventing any unauthorized data access or leakage between tenants.
• Performance: The enforcement of RLS policies should not significantly degrade database performance or application responsiveness.
• Maintainability: RLS policies and the logic for setting tenant context must be maintainable, with clear documentation for future adjustments.

Technical Specifications

Architecture Context

MeshHook utilizes a PostgreSQL database managed through Supabase, with SvelteKit serving the frontend and various server-side functionalities. The implementation of RLS will primarily involve PostgreSQL for policy definitions and enforcement, with necessary adjustments in the application layer for context setting.

Implementation Approach

1. Analysis:

   • Review the existing database schema and identify tables and views requiring RLS policies.
   • Determine the current method of tenant identification and context setting within the application.

2. Design:

   • Define RLS policies for each identified table/view, ensuring they enforce data isolation based on project_id.
   • Design the mechanism for dynamically setting the tenant context on each database connection/request.

3. Implementation:

   • Apply RLS policies to the database, using PostgreSQL's CREATE POLICY and related commands.
   • Implement the tenant context-setting logic within the application, ensuring it's applied to every database query.

4. Integration and Testing:

   • Perform thorough integration testing to verify that RLS policies are enforced as expected across all operations.
   • Validate performance impacts and optimize RLS policies and context-setting logic as necessary.

Data Model

No direct changes to the data model are required for the implementation of RLS policies. However, this task involves creating and applying RLS policies to existing tables and potentially adjusting indexes or views for performance optimization.

API Endpoints

No new API endpoints are required specifically for RLS policy enforcement. However, existing endpoints may require adjustments to ensure the correct tenant context is applied to all operations.

Acceptance Criteria

• RLS policies implemented for all identified tables and views, enforcing data isolation based on project_id.
• Mechanism for setting tenant context is integrated and functioning across all database operations.
• Integration tests confirm that RLS policies are correctly enforced, with no cross-tenant data access or leakage.
• Performance benchmarks indicate no significant degradation due to RLS policy enforcement.
• Documentation is updated to include details on RLS policy definitions and the context-setting mechanism.

Dependencies and Prerequisites

• Access to the MeshHook PostgreSQL database for policy implementation.
• Existing authentication and authorization mechanisms within MeshHook for tenant identification.

Implementation Notes

Development Guidelines

• Utilize PostgreSQL's native RLS features for policy definition and enforcement.
• Ensure that all code changes related to tenant context setting are modular and reusable across different parts of the application.

Testing Strategy

• Unit Tests: Verify the functionality of the tenant context-setting mechanism.
• Integration Tests: Test the enforcement of RLS policies across various operations and scenarios.

Security Considerations

• Verify that RLS policies correctly isolate tenant data, with rigorous testing of edge cases and potential bypass mechanisms.
• Ensure that the tenant context-setting mechanism securely identifies and applies the correct tenant context without vulnerabilities.

Monitoring & Observability

• Monitor database performance to identify any impacts from RLS policy enforcement.
• Implement logging for tenant context-setting operations to troubleshoot potential issues.

---

This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #141
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 141-rls-policy-enforcement.md
• 🎨 PlantUML Diagram: 141-rls-policy-enforcement.puml
• 🖼️ Diagram Image: 141-rls-policy-enforcement.png

---

This issue body was auto-generated from the PRD. Original issue content is preserved in the PRD document.
Last updated: 2025-10-10