Metadata-Version: 2.4
Name: ai-model-sentinel
Version: 0.1.3
Summary: Comprehensive AI Model Monitoring and Drift Detection Toolkit
Home-page: https://github.com/SalehAsaadAbughabraa/ai-model-sentinel
Author: Saleh Asaad Abughabraa
Author-email: saleh87alally@gmail.com
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE.txt
Requires-Dist: flask==2.3.3
Requires-Dist: flasgger==0.9.7.1
Requires-Dist: python-dotenv==1.0.0
Dynamic: author
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﻿# AI Model Sentinel 🔒

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

## 🌟 Table of Contents
- [Overview](#-overview)
- [Key Features](#-key-features)
- [Quick Start](#-quick-start)
- [Installation](#-installation)
- [API Documentation](#-api-documentation)
- [Architecture](#-architecture)
- [Contributing](#-contributing)
- [License](#-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

```bash
# 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.
