| # 🔒 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 | |
| --- | |
| **Note**: This security policy is reviewed quarterly and updated as needed. Last updated: January 2025. | |
| For questions about this security policy, contact: [email protected] |