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Easy RLS - AI Security Generator

Project Summary

Type: Portfolio / Demo Project
Focus: AI-Powered Code Generation

Key Features:

  • Natural language to SQL RLS policy generation
  • PostgreSQL Row-Level Security best practices
  • Validation and testing of generated policies
  • Developer-friendly interface

An AI-powered tool that simplifies database security by automatically generating PostgreSQL Row-Level Security (RLS) policies from natural language requirements.

The Problem

PostgreSQL Row-Level Security is powerful but notoriously difficult to implement correctly:

  • Complex syntax that's easy to get wrong
  • Security implications of misconfiguration are severe
  • Most developers avoid RLS due to the learning curve
  • Existing documentation is dense and technical

Architecture

flowchart LR
    subgraph input [User Input]
        nl[Natural Language Requirement]
        schema[Table Schema]
    end

    subgraph processing [AI Processing]
        parser[Requirement Parser]
        generator[Policy Generator]
        validator[Syntax Validator]
    end

    subgraph output [Output]
        policy[RLS Policy SQL]
        docs[Documentation]
        tests[Test Cases]
    end

    nl --> parser
    schema --> parser
    parser --> generator
    generator --> validator
    validator -->|valid| policy
    validator -->|invalid| generator
    policy --> docs
    policy --> tests

Technical Approach

AI-Powered Translation

The system uses LLMs to understand security requirements expressed in natural language and translates them into correct RLS policies. For example:

"Users should only see their own orders"

Becomes:

CREATE POLICY user_orders_policy ON orders
    FOR SELECT
    USING (user_id = current_user_id());

Validation Layer

Generated policies are validated against PostgreSQL syntax and common security patterns before being presented to the user. This catches errors before they reach production.

Best Practices Enforcement

The system incorporates PostgreSQL RLS best practices:

  • Proper policy naming conventions
  • Correct use of USING vs WITH CHECK clauses
  • Role-based access patterns
  • Multi-tenant isolation patterns

Results: Manual vs AI-Assisted

Metric Manual RLS Implementation Easy RLS
Time to first policy 30-60 min (docs + trial/error) < 2 min
Syntax errors Common Validated automatically
Best practices Often missed Enforced by default
Documentation Usually skipped Auto-generated

Tech Stack

Python OpenAI API PostgreSQL FastAPI Docker

Use Cases

  • Multi-tenant SaaS: Ensure tenants only see their own data
  • Healthcare: HIPAA-compliant access controls
  • Finance: Role-based access to sensitive financial data
  • E-commerce: Customer data isolation

Key Learnings

This project demonstrates how AI can lower the barrier to implementing complex security patterns. The same approach—AI-assisted code generation with validation—can be applied to many enterprise security challenges.

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