SECURITY.md

# 🔒 Security Policy - Cidadão.AI Models

## 📋 Overview

This document outlines the security practices and vulnerability disclosure process for the Cidadão.AI Models repository, which contains machine learning models and MLOps infrastructure for government transparency analysis.

## ⚠️ Supported Versions

| Version | Supported          |
| ------- | ------------------ |
| 1.0.x   | :white_check_mark: |

## 🛡️ Security Features

### ML Model Security
- **Model Integrity**: SHA-256 checksums for all model artifacts
- **Supply Chain Security**: Verified model provenance and lineage
- **Input Validation**: Robust validation of all model inputs
- **Output Sanitization**: Safe handling of model predictions
- **Adversarial Robustness**: Testing against adversarial attacks

### Data Security
- **Data Privacy**: Personal data anonymization in training datasets
- **LGPD Compliance**: Brazilian data protection law compliance
- **Secure Storage**: Encrypted storage of sensitive training data
- **Access Controls**: Role-based access to model artifacts
- **Audit Trails**: Complete logging of model training and deployment

### Infrastructure Security
- **Container Security**: Secure Docker images with minimal attack surface
- **Dependency Scanning**: Regular vulnerability scanning of Python packages
- **Secret Management**: Secure handling of API keys and model credentials
- **Network Security**: Encrypted communications for all model serving
- **Environment Isolation**: Separate environments for training and production

## 🚨 Reporting Security Vulnerabilities

### How to Report
1. **DO NOT** create a public GitHub issue for security vulnerabilities
2. Send an email to: **[email protected]** (or [email protected])
3. Include detailed information about the vulnerability
4. We will acknowledge receipt within 48 hours

### What to Include
- Description of the vulnerability
- Affected models or components
- Steps to reproduce the issue
- Potential impact on model performance or security
- Data samples (if safe to share)
- Suggested remediation (if available)
- Your contact information

### Response Timeline
- **Initial Response**: Within 48 hours
- **Investigation**: 1-7 days depending on severity
- **Model Retraining**: 1-14 days if required
- **Deployment**: 1-3 days after fix verification
- **Public Disclosure**: After fix is deployed (coordinated disclosure)

## 🛠️ Security Best Practices

### Model Development Security
```python
# Example secure model loading
import hashlib
import pickle

def secure_model_load(model_path, expected_hash):
    """Safely load model with integrity verification"""
    with open(model_path, 'rb') as f:
        model_data = f.read()
    
    # Verify model integrity
    model_hash = hashlib.sha256(model_data).hexdigest()
    if model_hash != expected_hash:
        raise SecurityError("Model integrity check failed")
    
    return pickle.loads(model_data)
```

### Data Handling Security
```python
# Example data anonymization
def anonymize_government_data(data):
    """Remove or hash personally identifiable information"""
    # Remove CPF, names, addresses
    # Hash vendor IDs
    # Preserve analytical utility while protecting privacy
    return anonymized_data
```

### Deployment Security
```bash
# Security checks before model deployment
pip audit                           # Check for vulnerable dependencies
bandit -r src/                     # Security linting
safety check                       # Known security vulnerabilities
docker scan cidadao-ai-models:latest # Container vulnerability scan
```

## 🔍 Security Testing

### Model Security Testing
- **Adversarial Testing**: Robustness against adversarial examples
- **Data Poisoning**: Detection of malicious training data
- **Model Extraction**: Protection against model stealing attacks
- **Membership Inference**: Privacy testing for training data
- **Fairness Testing**: Bias detection across demographic groups

### Infrastructure Testing
- **Penetration Testing**: Regular security assessments
- **Dependency Scanning**: Automated vulnerability detection
- **Container Security**: Image scanning and hardening
- **API Security**: Authentication and authorization testing
- **Network Security**: Encryption and secure communications

## 🎯 Model-Specific Security Considerations

### Corruption Detection Models
- **False Positive Impact**: Careful calibration to minimize false accusations
- **Bias Prevention**: Regular testing for demographic and regional bias
- **Transparency**: Explainable AI for all corruption predictions
- **Audit Trail**: Complete logging of all corruption detections

### Anomaly Detection Models
- **Threshold Management**: Secure configuration of anomaly thresholds
- **Feature Security**: Protection of sensitive features from exposure
- **Model Drift**: Monitoring for performance degradation over time
- **Validation**: Human expert validation of anomaly predictions

### Natural Language Models
- **Text Sanitization**: Safe handling of government document text
- **Information Extraction**: Secure extraction without data leakage
- **Language Security**: Protection against prompt injection attacks
- **Content Filtering**: Removal of personally identifiable information

## 📊 Privacy and Ethics

### Data Privacy
- **Anonymization**: Personal data removed or hashed in all models
- **Minimal Collection**: Only necessary data used for model training
- **Retention Limits**: Training data deleted after model deployment
- **Access Logs**: Complete audit trail of data access
- **Consent Management**: Respect for data subject rights under LGPD

### Ethical AI
- **Fairness**: Regular bias testing and mitigation
- **Transparency**: Explainable predictions for all model outputs
- **Accountability**: Clear responsibility for model decisions
- **Human Oversight**: Human review required for high-impact predictions
- **Social Impact**: Assessment of model impact on society

## 📞 Contact Information

### Security Team
- **Primary Contact**: [email protected]
- **ML Security**: [email protected] (or [email protected])
- **Data Privacy**: [email protected] (or [email protected])
- **Response SLA**: 48 hours for critical model security issues

### Emergency Contact
For critical security incidents affecting production models:
- **Email**: [email protected] (Priority: CRITICAL)
- **Subject**: [URGENT ML SECURITY] Brief description

## 🔬 Model Governance

### Model Registry Security
- **Version Control**: Secure versioning of all model artifacts
- **Access Control**: Role-based access to model registry
- **Audit Logging**: Complete history of model updates
- **Approval Process**: Required approval for production deployments

### Monitoring and Alerting
- **Performance Monitoring**: Real-time model performance tracking
- **Security Monitoring**: Detection of anomalous model behavior
- **Data Drift Detection**: Monitoring for changes in input distributions
- **Alert System**: Immediate notification of security incidents

## 📚 Security Resources

### ML Security Documentation
- [OWASP Machine Learning Security Top 10](https://owasp.org/www-project-machine-learning-security-top-10/)
- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)
- [Google ML Security Best Practices](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

### Security Tools
- **Model Scanning**: TensorFlow Privacy, PyTorch Security
- **Data Validation**: TensorFlow Data Validation (TFDV)
- **Bias Detection**: Fairness Indicators, AI Fairness 360
- **Adversarial Testing**: Foolbox, CleverHans

## 🔄 Incident Response

### Model Security Incidents
1. **Immediate Response**: Isolate affected models from production
2. **Assessment**: Evaluate impact and scope of security breach
3. **Containment**: Prevent further damage or data exposure
4. **Investigation**: Determine root cause and affected systems
5. **Recovery**: Retrain or redeploy secure models
6. **Post-Incident**: Review and improve security measures

### Communication Plan
- **Internal**: Immediate notification to security team and stakeholders
- **External**: Coordinated disclosure to affected users and regulators
- **Public**: Transparent communication about resolved issues

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**Note**: This security policy is reviewed quarterly and updated as needed. Last updated: January 2025.

For questions about this security policy, contact: [email protected]