How to Build an AI-Powered Code Security Auditor

Simple Summary

An intelligent code security auditor that leverages AI to automatically detect and report potential vulnerabilities in software projects, enhancing development security and efficiency.

Product Requirements Document (PRD)

Goals:

• Create an intelligent code security auditor that can analyze multiple programming languages
• Provide detailed reports on potential vulnerabilities with severity ratings
• Offer suggestions for fixing identified security issues
• Integrate with popular version control systems and CI/CD pipelines

Target Audience:

• Software developers
• Security professionals
• DevOps teams

Key Features:

1. AI-powered code analysis
2. Multi-language support
3. Customizable security rules
4. Integration with Git repositories
5. Detailed vulnerability reports
6. Remediation suggestions
7. CI/CD pipeline integration
8. User-friendly dashboard

User Requirements:

• Easy-to-use interface for uploading or connecting code repositories
• Ability to customize security rules and sensitivity levels
• Clear, actionable reports on identified vulnerabilities
• Integration with existing development workflows
• Regular updates to the AI model to detect new types of vulnerabilities

User Flows

1. Repository Analysis:

   • User logs in
   • Connects GitHub/GitLab account or uploads code
   • Initiates security scan
   • Views detailed report of vulnerabilities
   • Accesses remediation suggestions

2. CI/CD Integration:

   • User configures integration with CI/CD pipeline
   • Code is automatically scanned on each commit
   • Results are displayed in pipeline dashboard
   • Notifications sent for critical vulnerabilities

3. Custom Rule Management:

   • User navigates to rule configuration page
   • Creates or modifies security rules
   • Tests rules against sample code
   • Applies rules to future scans

Technical Specifications

• Frontend: React with TypeScript
• Backend: Node.js with Express
• Database: PostgreSQL
• AI/ML: TensorFlow.js for vulnerability detection
• API: RESTful architecture
• Authentication: JWT with OAuth for Git integration
• CI/CD: Jenkins or GitLab CI
• Containerization: Docker
• Cloud Hosting: AWS or Google Cloud Platform

API Endpoints

• POST /api/auth/register
• POST /api/auth/login
• GET /api/projects
• POST /api/projects/scan
• GET /api/reports/{reportId}
• PUT /api/settings/rules
• GET /api/integrations/git
• POST /api/webhooks/ci

Database Schema

Users:

• id (PK)
• email
• password_hash
• created_at
• last_login

Projects:

• id (PK)
• user_id (FK)
• name
• repository_url
• last_scan_date

ScanReports:

• id (PK)
• project_id (FK)
• scan_date
• vulnerability_count
• report_data (JSON)

CustomRules:

• id (PK)
• user_id (FK)
• rule_name
• rule_definition
• severity

File Structure

/src /components /Dashboard /ScanReport /RuleEditor /pages Home.tsx Login.tsx Project.tsx Settings.tsx /api auth.ts projects.ts reports.ts rules.ts /utils aiAnalyzer.ts gitIntegration.ts /styles global.css /public /assets logo.svg icons/ /server /routes /models /controllers /middleware /tests /unit /integration README.md package.json tsconfig.json Dockerfile .env.example

Implementation Plan

1. Project Setup (1 week)

   • Initialize repository and project structure
   • Set up development environment and tools

2. Backend Development (3 weeks)

   • Implement user authentication and authorization
   • Develop core API endpoints
   • Create database models and migrations

3. AI Model Integration (2 weeks)

   • Research and select appropriate AI/ML libraries
   • Implement code analysis algorithms
   • Train initial model on common vulnerabilities

4. Frontend Development (3 weeks)

   • Create main application components
   • Implement user interface for scanning and reporting
   • Develop dashboard and settings pages

5. Git Integration (1 week)

   • Implement OAuth flow for Git providers
   • Develop repository scanning functionality

6. CI/CD Integration (1 week)

   • Create plugins/extensions for popular CI/CD tools
   • Implement webhook handlers for automated scanning

7. Testing and Refinement (2 weeks)

   • Conduct thorough testing of all features
   • Refine AI model based on test results
   • Optimize performance and fix bugs

8. Documentation and Deployment (1 week)

   • Write user and API documentation
   • Prepare deployment scripts and configurations
   • Deploy to production environment

Deployment Strategy

1. Containerize application using Docker
2. Set up Kubernetes cluster on chosen cloud provider (AWS EKS or GCP GKE)
3. Configure CI/CD pipeline for automated testing and deployment
4. Use Helm charts for managing Kubernetes deployments
5. Implement auto-scaling based on load
6. Set up monitoring and logging (e.g., Prometheus, Grafana, ELK stack)
7. Configure regular database backups
8. Implement blue-green deployment strategy for zero-downtime updates

Design Rationale

• React and TypeScript chosen for a robust, type-safe frontend development experience
• Node.js backend for JavaScript consistency across the stack and excellent package ecosystem
• PostgreSQL selected for its reliability and support for JSON data types (useful for storing scan reports)
• TensorFlow.js enables AI functionality to run both server-side and potentially client-side for faster analysis
• RESTful API design for wide compatibility and ease of integration
• Docker and Kubernetes for scalability and ease of deployment across different environments
• Modular file structure to promote code reusability and easier maintenance