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GitHub Actions for VPN Deploy Management

Overview

This repository now includes a comprehensive set of GitHub Actions workflows for automated VPN deployment, testing, security scanning, and monitoring. The workflows are designed for a multi-environment deployment strategy with staging and production environments.

Workflow Files Created

1. deploy-vpn.yml - Main Deployment Pipeline

Triggers:

  • Push to main or develop branches
  • Pull requests to main
  • Manual workflow dispatch

Features:

  • Multi-stage deployment (staging → production)
  • Code quality checks (Black, Flake8, MyPy)
  • Automated testing with coverage reports
  • Docker image building and testing
  • Security pre-checks using Bandit and Safety
  • Infrastructure validation
  • Blue/green deployment strategy
  • Automatic rollback on failure
  • Deployment notifications via Slack

Key Jobs:

  • Pre-deployment checks
  • API building and testing
  • Dashboard building and testing
  • Infrastructure validation
  • Staging deployment
  • Production deployment

2. security-scan.yml - Automated Security Scanning

Triggers:

  • Daily schedule (2 AM UTC)
  • Push to main or develop
  • Pull requests
  • Manual workflow dispatch

Features:

  • Dependency vulnerability scanning (Python & Node.js)
  • Container security scanning with Trivy
  • Code security analysis (Bandit, Semgrep, CodeQL)
  • Infrastructure security checks
  • VPN-specific security validations
  • Comprehensive security reporting
  • Automated security alerts

Key Jobs:

  • Dependency scanning
  • Container image scanning
  • Code security analysis
  • Infrastructure security validation
  • VPN security configuration checks
  • Security summary generation

3. test-vpn.yml - VPN Testing & Validation

Triggers:

  • Push to main or develop
  • Pull requests
  • Every 6 hours (scheduled)
  • Manual workflow dispatch

Features:

  • Comprehensive test suite execution
  • Unit tests for all components
  • Integration tests with mock VPN servers
  • Performance testing and benchmarking
  • Security testing
  • Smoke tests for basic functionality
  • End-to-end testing with Playwright
  • Automated test reporting

Test Suites:

  • Unit tests (Python modules)
  • Integration tests (API, database, Docker)
  • Performance tests (API response times, config generation)
  • Security tests (cryptographic strength, API security)
  • Smoke tests (basic functionality)
  • E2E tests (dashboard automation)

4. update-dashboard.yml - Dashboard Deployment Automation

Triggers:

  • Push to main or develop (dashboard files only)
  • Pull requests (dashboard files only)
  • Manual workflow dispatch

Features:

  • Dashboard-specific build pipeline
  • Performance testing with Lighthouse
  • Multiple deployment targets (Vercel, Netlify, custom servers)
  • Automated smoke testing
  • Bundle size analysis
  • Production monitoring and alerting
  • Deployment rollback capabilities
  • Environment-specific configurations

Deployment Options:

  • Vercel deployment
  • Netlify deployment
  • Custom server deployment (rsync)
  • AWS S3/CloudFront deployment

5. monitoring-deploy.yml - Monitoring Stack Deployment

Triggers:

  • Push to main or develop (monitoring files)
  • Hourly health checks (scheduled)
  • Manual workflow dispatch

Features:

  • Monitoring stack deployment (Prometheus, Grafana, VPN API)
  • Docker image building and security scanning
  • Multi-environment deployment
  • Health check automation
  • Alert system testing
  • Log rotation and cleanup
  • Performance monitoring
  • Automated status reporting

Monitoring Components:

  • Prometheus (metrics collection)
  • Grafana (visualization)
  • VPN Monitoring API
  • Health check system
  • Alert management
  • Log aggregation

Environment Variables Required

GitHub Secrets

Configure these secrets in your GitHub repository settings:

# Staging Environment
STAGING_SUPABASE_URL=
STAGING_SUPABASE_SERVICE_ROLE_KEY=
STAGING_GRAFANA_ADMIN_PASSWORD=
STAGING_SLACK_WEBHOOK_URL=
STAGING_DASHBOARD_URL=
STAGING_HOST=
STAGING_USER=
STAGING_S3_BUCKET=

# Production Environment
PRODUCTION_SUPABASE_URL=
PRODUCTION_SUPABASE_SERVICE_ROLE_KEY=
PRODUCTION_GRAFANA_ADMIN_PASSWORD=
PRODUCTION_SLACK_WEBHOOK_URL=
PRODUCTION_DASHBOARD_URL=

# Deployment Platforms
VERCEL_TOKEN=
NETLIFY_AUTH_TOKEN=
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=

Deployment Flow

Development Workflow

  1. Developer pushes code to develop branch
  2. deploy-vpn.yml triggers and deploys to staging
  3. test-vpn.yml runs comprehensive tests
  4. security-scan.yml performs security checks
  5. Dashboard deploys to staging environment
  6. Monitoring stack deploys to staging
  7. All results are reported via Slack

Production Workflow

  1. Code is merged to main branch
  2. deploy-vpn.yml triggers production deployment
  3. test-vpn.yml runs full test suite
  4. security-scan.yml performs comprehensive security scan
  5. Dashboard deploys to production
  6. Monitoring stack deploys to production
  7. Production health checks begin
  8. Deployment status is reported

Scheduled Operations

  • Daily Security Scans (2 AM UTC)
  • Comprehensive Testing (Every 6 hours)
  • Health Checks (Every hour)
  • Monitoring Stack Validation (Every hour)

Key Features

Security

  • Multi-layer security scanning
  • Container vulnerability assessment
  • Code security analysis
  • Secret detection
  • Cryptographic strength validation
  • Automated security reporting

Testing

  • Unit, integration, and E2E testing
  • Performance benchmarking
  • Security testing
  • Automated test coverage reporting
  • Cross-platform compatibility testing

Deployment

  • Blue/green deployment strategy
  • Automatic rollback on failure
  • Zero-downtime deployments
  • Multi-environment support
  • Configuration management

Monitoring

  • Real-time health monitoring
  • Performance tracking
  • Alert management
  • Log aggregation
  • Automated maintenance

Notifications

  • Slack integration for all major events
  • Detailed deployment reports
  • Security scan results
  • Test summaries
  • Health check status

Usage Instructions

Manual Deployment

To manually trigger a deployment:

  1. Go to the Actions tab in your GitHub repository
  2. Select the desired workflow
  3. Click Run workflow
  4. Choose the environment and options
  5. Click Run workflow to start

Monitoring Deployments

All deployments can be monitored through:

  • GitHub Actions tab
  • Slack notifications
  • Deployment reports (uploaded as artifacts)
  • Monitoring dashboard (Grafana)

Viewing Reports

Workflow artifacts are automatically uploaded and include:

  • Security scan reports
  • Test coverage reports
  • Deployment summaries
  • Monitoring status reports
  • Performance analysis

Customization

Each workflow can be customized by:

  • Modifying trigger conditions
  • Adjusting testing parameters
  • Changing deployment targets
  • Updating security scan rules
  • Customizing notification channels

Troubleshooting

Common Issues

  1. Deployment failures: Check the deployment logs and verify environment variables
  2. Security scan failures: Review security reports and address critical issues
  3. Test failures: Check test logs and fix failing tests before deployment
  4. Monitoring issues: Verify monitoring stack health in Grafana dashboard

Logs and Debugging

  • All workflow logs are available in the GitHub Actions tab
  • Artifacts contain detailed reports for debugging
  • Slack notifications provide real-time status updates
  • Monitoring dashboard shows system health metrics

Next Steps

  1. Configure GitHub Secrets in your repository settings
  2. Test workflows on the develop branch first
  3. Customize notifications and deployment targets as needed
  4. Set up monitoring dashboards and alert rules
  5. Review and adjust security scan parameters
  6. Establish deployment schedules and maintenance windows

The workflows are designed to be production-ready and can be customized further based on your specific requirements and infrastructure setup.

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