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

# 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

# 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

# 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
• NIST AI Risk Management Framework
• Google ML Security Best Practices

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]