AI Agents in Cybersecurity - Complete Code Repository

📚 About This Repository

This repository contains all the source code, examples, and production-ready implementations from the book "AI Agents in Cybersecurity: A Practitioner's Guide. From Strategy to Implementation" by Nicolas Cravino (2025).

Whether you're a security analyst looking to automate SOC operations, a CISO planning AI strategy, or an engineer building defensive systems, this repository provides practical, tested code you can deploy immediately.

🚀 Quick Start

Get your first AI security agent running in 30 minutes:

# Clone the repository
git clone https://github.com/ai-agents-cybersecurity/complete.git
cd complete

# Install dependencies
pip install -r requirements.txt

# Run the quick-start agent
cd quick-start
python alert_triage_agent.py

📂 Repository Structure

ai-agents-cybersecurity/
├── quick-start/           # 30-minute agent from Appendix A
├── chapter-examples/      # Code organized by book chapter
│   ├── ch01-strategic/    # Strategic implementation examples
│   ├── ch02-concepts/     # Core concepts and SPAR framework
│   ├── ch03-architectures/# Agent architecture patterns
│   ├── ch04-oversight/    # Human oversight (HITL/HOTL/HIC)
│   ├── ch05-scaling/      # Enterprise scaling solutions
│   ├── ch06-digital-twins/# Security simulations
│   ├── ch07-predictive/   # Predictive defense systems
│   ├── ch08-identity/     # Behavioral analytics
│   ├── ch09-explainable/  # Explainable AI (XAI)
│   ├── ch10-governance/   # Ethics and compliance
│   ├── ch11-operations/   # Operationalizing agents
│   ├── ch12-soc/         # SOC integration
│   ├── ch13-monitoring/   # Monitoring and maintenance
│   ├── ch14-trends/      # Future trends and roadmap
│   ├── ch15-threats/     # Attack surface analysis
│   └── ch16-hardening/   # Security hardening
├── production-ready/      # Full production implementations
│   ├── alert-triage/     # Complete alert triage system
│   ├── threat-hunting/   # Autonomous threat hunter
│   ├── incident-response/# IR orchestration platform
│   └── vulnerability-mgmt/# Predictive patching system
├── integrations/         # Third-party integrations
│   ├── splunk/          # Splunk apps and dashboards
│   ├── elastic/         # Elasticsearch integrations
│   ├── sentinel/        # Microsoft Sentinel playbooks
│   └── crowdstrike/     # CrowdStrike EDR integration
├── docker/              # Containerized deployments
├── kubernetes/          # K8s manifests and Helm charts
├── terraform/          # Infrastructure as Code
└── tests/             # Comprehensive test suites

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• Docker (for containerized deployments)
• Kubernetes cluster (for production deployments)
• API keys for AI services (OpenAI, Anthropic, etc.)

Basic Installation

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install core dependencies
pip install -r requirements.txt

# Install development dependencies
pip install -r requirements-dev.txt

Production Installation

# Install all production dependencies
pip install -r requirements-prod.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys and configuration

# Run database migrations
python manage.py migrate

# Start the orchestrator
python orchestrator/main.py

🔑 Key Features

Core Agent Implementations

• SPAR Framework: Complete implementation of Sense-Plan-Act-Reflect lifecycle
• Multi-Agent Systems: Collaborative agent architectures
• Human Oversight: HITL, HOTL, and HIC patterns with kill switches
• Explainable AI: SHAP and LIME implementations for security decisions

Production Systems

• Alert Triage: Reduce false positives by 70%
• Threat Hunting: Autonomous threat detection with MITRE ATT&CK mapping
• Incident Response: Automated playbook execution with human gates
• Vulnerability Management: Predictive patching with EPSS integration

Enterprise Integrations

• SIEM platforms (Splunk, QRadar, Sentinel)
• EDR solutions (CrowdStrike, SentinelOne)
• Cloud providers (AWS, Azure, GCP)
• Ticketing systems (ServiceNow, Jira)

📖 Documentation

Chapter Code Examples

Each chapter folder contains:

• README.md - Chapter overview and learning objectives
• Code examples with inline documentation
• Configuration files and templates
• Test cases and validation scripts

API Documentation

Comprehensive API documentation is available at /docs/api/ including:

• Agent orchestration APIs
• Integration endpoints
• Webhook configurations
• Authentication methods

🧪 Testing

# Run unit tests
pytest tests/unit/

# Run integration tests
pytest tests/integration/

# Run security tests
python tests/security/vulnerability_scan.py

# Run performance tests
locust -f tests/performance/load_test.py

🚢 Deployment

Docker Deployment

# Build the container
docker build -t ai-security-agent .

# Run with docker-compose
docker-compose up -d

Kubernetes Deployment

# Deploy with kubectl
kubectl apply -f kubernetes/

# Or use Helm
helm install security-agent ./helm-charts/security-agent

Cloud Deployment

Terraform configurations for:

• AWS ECS/EKS deployment
• Azure Container Instances/AKS
• Google Cloud Run/GKE

📊 Performance Metrics

Based on production deployments:

• Alert Processing: 10,000+ alerts/minute
• Response Time: <100ms for triage decisions
• Accuracy: 94% true positive rate
• Uptime: 99.99% availability SLA

🔒 Security Considerations

• All code follows OWASP secure coding practices
• API keys and secrets managed via environment variables
• Role-based access control (RBAC) implemented
• Audit logging for all agent decisions
• Encrypted communication channels
• Regular dependency updates

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for:

• Code of conduct
• Development setup
• Pull request process
• Coding standards

📚 Learning Path

For Beginners

1. Start with quick-start/ - Get an agent running
2. Study chapter-examples/ch02-concepts/ - Understand core concepts
3. Explore chapter-examples/ch03-architectures/ - Learn patterns

For Practitioners

1. Review production-ready/ - Full implementations
2. Examine integrations/ - Connect to your stack
3. Deploy with docker/ or kubernetes/

For Researchers

1. Explore chapter-examples/ch15-threats/ - Attack patterns
2. Study chapter-examples/ch09-explainable/ - XAI methods
3. Contribute to tests/ - Improve validation

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

Third-Party Licenses

• MITRE ATT&CK® - Apache 2.0
• SHAP - MIT License
• LangChain - MIT License
• See THIRD_PARTY_LICENSES.md for complete list

🙏 Acknowledgments

• The cybersecurity community for continuous feedback
• Contributors who improved the code
• Organizations that tested in production
• Open source projects we build upon

📮 Support

• Book Website: https://aiagentscybersecurity.com
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support@aiagentscybersecurity.com

🚦 Status

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⚡ Ready to transform your security operations with AI? Start with the quick-start guide and have your first agent running in 30 minutes!

"The future of cybersecurity isn't human OR machine—it's human AND machine, working together at unprecedented scale and speed." - Nicolas Cravino