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Production Deployment Checklist

✅ Pre-Deployment Checklist

Repository Preparation

  • All Python cache files (__pycache__, *.pyc) removed
  • All ML model files (*.pkl) removed (will be regenerated)
  • All log files cleaned up
  • Database files removed for fresh start
  • README.md files added to all empty directories
  • .gitignore configured for production
  • Deployment script created (deploy.sh)

Directory Structure

  • backups/ - Backup storage with README
  • data/captures/ - Network packet captures with README
  • data/honeypots/ - Honeypot data with README
  • data/models/ - ML model storage with README
  • data/reports/ - Security reports with README
  • data/sandbox/ - Malware analysis with README
  • data/threat_intel/ - Threat intelligence with README
  • exports/ - Data exports with README
  • logs/ - System logs with README
  • models/ - Trained models with README
  • temp/ - Temporary files with README

Configuration

  • Copy config/config.example.yaml to config/config.yaml
  • Configure database settings
  • Set up network interface monitoring
  • Configure honeypot ports
  • Set threat intelligence feeds
  • Configure ML model parameters

Environment Variables

Set these environment variables before deployment:

export SHADOWWALL_SECRET_KEY="your-secret-key-here"
export SHADOWWALL_DB_PATH="data/shadowwall.db"
export SHADOWWALL_LOG_LEVEL="INFO"
export SHADOWWALL_DASHBOARD_HOST="0.0.0.0"
export SHADOWWALL_DASHBOARD_PORT="8081"

Security Setup

  • Generate secure secret keys
  • Configure firewall rules
  • Set up SSL/TLS certificates (if needed)
  • Configure user authentication
  • Set appropriate file permissions

Network Configuration

  • Ensure required ports are available:
    • 8081 (Dashboard)
    • 2200 (SSH Honeypot)
    • 8000 (HTTP Honeypot)
    • 2100 (FTP Honeypot)
  • Configure network interface access
  • Set up packet capture permissions

🚀 Deployment Steps

  1. Clone Repository

    git clone https://github.com/yashab-cyber/shadow-wall.git
cd shadow-wall

  2. Run Deployment Script

    chmod +x deploy.sh
./deploy.sh

  3. Configure System

    cp config/config.example.yaml config/config.yaml
# Edit config.yaml with your settings

  4. Set Environment Variables

    export SHADOWWALL_SECRET_KEY="$(openssl rand -hex 32)"
# Set other required variables

  5. Start System

    python run_integrated.py

  6. Verify Deployment

    • Dashboard accessible at http://localhost:8081
    • All ML models training successfully
    • Network monitoring active
    • Honeypots deployed
    • No critical errors in logs

📋 Post-Deployment

Monitoring

  • Set up log monitoring
  • Configure alerting
  • Monitor system performance
  • Verify ML model training

Maintenance

  • Set up automated backups
  • Configure log rotation
  • Schedule model retraining
  • Plan capacity monitoring

Documentation

  • Update team on access procedures
  • Document custom configurations
  • Create operational runbooks
  • Set up incident response procedures

🔍 Troubleshooting

Common Issues

  1. Permission Errors: Check file permissions on data directories
  2. Port Conflicts: Ensure required ports are not in use
  3. Missing Dependencies: Run pip install -r requirements.txt
  4. Configuration Errors: Verify config.yaml syntax
  5. Network Issues: Check interface permissions for packet capture

Support

  • Check logs in logs/ directory
  • Review configuration in config/config.yaml
  • Consult README.md and documentation files
  • Check GitHub issues for known problems

✅ Production Ready

When all items are checked, the ShadowWall AI system is ready for production deployment!

Last updated: August 3, 2025