ai-model-sentinel 0.1.3

Comprehensive AI Model Monitoring and Drift Detection Toolkit

AI Model Sentinel 🔒

Enterprise-grade security framework for protecting AI models against sophisticated threats, inference attacks, and data extraction attempts.

🌟 Table of Contents

• Overview
• Key Features
• Quick Start
• Installation
• API Documentation
• Architecture
• Contributing
• License

📖 Overview

AI Model Sentinel is a comprehensive security framework designed to protect machine learning models from various threats including model inversion attacks, membership inference attacks, adversarial examples, and data extraction attempts.

🚀 Key Features

🔒 Advanced Protection Mechanisms

• AI-Powered Honeytoken System: Dynamic bait generation and trap placement
• Real-time Threat Detection: Behavioral analysis and anomaly detection
• Adaptive Defense: Machine learning-based security adaptation
• Zero Trust Architecture: Verify everything, trust nothing

🌐 Global Security Network

• Community Threat Intelligence: Shared security insights across all users
• Collective Defense: Collaborative protection mechanism
• Real-time Updates: Immediate threat response and updates

🛠️ Enterprise Ready

• Prometheus Integration: Production-grade monitoring and metrics
• Enterprise Dashboard: Comprehensive management interface
• RESTful API: Full programmatic control and integration

⚡ Quick Start

# Install via npm
npm install ai-model-sentinel

# Install via pip
pip install ai-model-sentinel

# Or clone from source
git clone https://github.com/SalehAsaadAbughabraa/ai-model-sentinel.git
cd ai-model-sentinel
pip install -r requirements.txt
Basic Usage
python
from ai_model_sentinel import SentinelClient, SecurityConfig

# Initialize with default configuration
config = SecurityConfig(
    api_key="your-api-key",
    security_level="high",
    enable_honeytokens=True
)

sentinel = SentinelClient(config)

# Protect your model inference
def protected_inference(model, input_data):
    threat_analysis = sentinel.analyze_input(input_data)
    
    if threat_analysis.is_malicious:
        raise SecurityException("Potential threat detected")
    
    predictions = model.predict(input_data)
    protected_output = sentinel.protect_output(input_data, predictions)
    
    return protected_output
📦 Installation Details
NPM Package
json
{
  "dependencies": {
    "ai-model-sentinel": "^0.1.0"
  }
}
Python Package
python
# requirements.txt
ai-model-sentinel>=0.1.0
📚 API Documentation
SentinelClient Class
python
class SentinelClient:
    def __init__(self, config: SecurityConfig):
        """Initialize the security sentinel."""
    
    def analyze_input(self, input_data: Any) -> ThreatAnalysis:
        """Analyze input data for potential threats."""
    
    def protect_output(self, input_data: Any, predictions: Any) -> ProtectedOutput:
        """Apply protection layers to model output."""
🏗️ Architecture
text
ai-model-sentinel/
├── src/
│   ├── core/                 # Core security infrastructure
│   ├── honeytoken/           # Honeytoken system
│   ├── api/                  # REST API layer
│   ├── monitoring/           # Monitoring system
│   └── utils/                # Utilities
├── tests/                    # Comprehensive test suite
├── examples/                 # Usage examples
└── docs/                     # Documentation
🤝 Contributing
We welcome contributions! Please see our contributing guidelines:

Fork the repository

Create a feature branch

Commit your changes

Push to the branch

Open a Pull Request

📄 License
MIT License - see LICENSE file for details.

🆘 Support
GitHub Issues: Report Bugs

Documentation: Read the Docs

🙏 Acknowledgments
Research teams advancing AI security

Open source community contributions

Security researchers worldwide

Note: This is an alpha release. Features and APIs may change during development.