Security Policy for CardioPredict Pro

🔒 Security Overview

CardioPredict Pro handles sensitive medical information and requires robust security measures. This document outlines our security practices, vulnerability reporting process, and guidelines for secure usage.

🏥 Medical Data Security

Data Handling Principles

No Storage by Default: The application doesn't store patient data unless explicitly configured
Encryption in Transit: All data transmission uses HTTPS/TLS encryption
Temporary Processing: Patient data exists only during the prediction process
Optional Persistence: Database integration is optional and requires explicit setup

HIPAA Considerations

While CardioPredict Pro is designed for educational use, organizations using it with real patient data should consider:

Business Associate Agreements (BAA): Required for production medical use
Access Controls: Implement proper user authentication and authorization
Audit Trails: Log all access and predictions for compliance
Data Minimization: Only collect necessary clinical parameters

🛡️ Supported Versions

We provide security updates for the following versions:

Version	Supported	Security Updates
1.0.x	✅ Current	✅ Active
0.9.x	✅ LTS	✅ Critical Only
< 0.9	❌ End of Life	❌ None

🚨 Vulnerability Categories

Critical Vulnerabilities

Patient Data Exposure: Unauthorized access to medical information
Model Manipulation: Attacks affecting prediction accuracy
Authentication Bypass: Unauthorized system access
Code Injection: SQL injection, XSS, or code execution vulnerabilities

High-Priority Vulnerabilities

Denial of Service: Attacks affecting system availability
Privilege Escalation: Unauthorized permission increases
Data Integrity: Unauthorized modification of predictions or reports
Session Management: Issues with user session handling

Medium-Priority Vulnerabilities

Information Disclosure: Non-critical information leaks
CSRF: Cross-site request forgery vulnerabilities
Input Validation: Improper handling of malicious inputs
Dependency Issues: Security issues in third-party packages

📧 Reporting Vulnerabilities

How to Report

Send security vulnerabilities to: security@raghav0079.dev

Please include:

Vulnerability Description: Detailed explanation of the issue
Reproduction Steps: Clear steps to reproduce the vulnerability
Impact Assessment: Potential medical and security implications
Proof of Concept: Evidence of the vulnerability (if safe to share)
Suggested Fix: Recommendations for resolution (if known)

Response Timeline

Acknowledgment: Within 24 hours
Initial Assessment: Within 72 hours
Status Updates: Every 7 days until resolution
Fix Deployment: Critical issues within 7 days, others within 30 days

Responsible Disclosure

90-Day Policy: We aim to resolve issues within 90 days
Coordinated Disclosure: We'll work with you on disclosure timing
Public Recognition: Contributors will be credited (unless they prefer anonymity)
No Legal Action: We won't pursue legal action for good-faith security research

🔐 Security Best Practices

For Developers

Secure Coding

# Input validation for medical parameters
def validate_medical_input(age, bp_systolic, cholesterol):
    """Validate medical inputs to prevent injection attacks"""
    try:
        age = int(age)
        bp_systolic = int(bp_systolic)
        cholesterol = int(cholesterol)
    except ValueError:
        raise SecurityError("Invalid medical parameter format")
    
    # Range validation for medical safety
    if not (18 <= age <= 120):
        raise SecurityError("Age outside valid medical range")
    if not (70 <= bp_systolic <= 250):
        raise SecurityError("Blood pressure outside valid range")
    if not (100 <= cholesterol <= 600):
        raise SecurityError("Cholesterol outside valid range")
    
    return age, bp_systolic, cholesterol

# Secure database queries (if using database features)
def secure_patient_query(patient_id):
    """Use parameterized queries to prevent SQL injection"""
    query = "SELECT * FROM patients WHERE id = %s AND active = true"
    return execute_query(query, (patient_id,))

Environment Security

# Use environment variables for sensitive data
export SUPABASE_URL="your_secure_url"
export SUPABASE_KEY="your_secure_key"
export WANDB_API_KEY="your_api_key"

# Never commit secrets to version control
echo "*.env" >> .gitignore
echo "*.key" >> .gitignore
echo "credentials.json" >> .gitignore

Dependency Management

# Regular security updates
pip install --upgrade pip
pip audit  # Check for known vulnerabilities
pip install safety && safety check

# Pin secure versions
# In requirements.txt
gradio==5.49.1  # Pinned version
pandas>=2.3.3,<3.0.0  # Version range

For Deployment

Hugging Face Spaces Security

# In README.md metadata for HF Spaces
---
title: CardioPredict Pro
emoji: 🫀
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
short_description: AI cardiovascular risk assessment - Educational use only
---

Production Deployment Security

# Secure configuration for production
import os
import secrets

# Generate secure session keys
SECRET_KEY = secrets.token_urlsafe(32)

# Configure secure headers
SECURE_HEADERS = {
    'X-Content-Type-Options': 'nosniff',
    'X-Frame-Options': 'DENY',
    'X-XSS-Protection': '1; mode=block',
    'Strict-Transport-Security': 'max-age=31536000; includeSubDomains',
    'Content-Security-Policy': "default-src 'self'"
}

# Database security
DATABASE_CONFIG = {
    'host': os.getenv('DB_HOST'),
    'port': int(os.getenv('DB_PORT', 5432)),
    'database': os.getenv('DB_NAME'),
    'user': os.getenv('DB_USER'),
    'password': os.getenv('DB_PASSWORD'),
    'sslmode': 'require',
    'connect_timeout': 10,
    'command_timeout': 30
}

For Users

Safe Usage Guidelines

Educational Use Only: Never use for actual medical decisions
Synthetic Data: Only use fake/synthetic patient data for testing
Secure Environment: Use trusted networks and devices
Regular Updates: Keep the application updated to latest version
Professional Review: Have qualified professionals review any outputs

Network Security

# Use HTTPS only
https://your-deployment-url.com

# Avoid public networks for sensitive testing
# Use VPN or secure networks when testing

# Verify SSL certificates
curl -I https://your-deployment-url.com

Data Protection

No Real Patients: Never enter real patient information
Screen Privacy: Ensure screen privacy in public spaces
Session Management: Log out when finished
Clear Browser Data: Clear medical data from browser cache

🔍 Security Monitoring

Automated Security Checks

GitHub Security Features

Dependabot: Automated dependency vulnerability scanning
CodeQL: Static code analysis for security issues
Secret Scanning: Detection of accidentally committed secrets
Security Advisories: Community-reported vulnerability tracking

Continuous Integration Security

# .github/workflows/security.yml
name: Security Checks
on: [push, pull_request]

jobs:
  security:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    
    - name: Setup Python
      uses: actions/setup-python@v3
      with:
        python-version: '3.9'
    
    - name: Install dependencies
      run: |
        pip install safety bandit semgrep
        pip install -r requirements.txt
    
    - name: Run safety check
      run: safety check
    
    - name: Run bandit security scan
      run: bandit -r . -x tests/
    
    - name: Run semgrep security scan
      run: semgrep --config=auto .

Manual Security Audits

Monthly Reviews: Regular code and configuration reviews
Penetration Testing: Quarterly security assessments
Medical Safety Reviews: Ongoing clinical validation
Dependency Audits: Regular third-party package reviews

🚫 Security Scope Exclusions

Out of Scope

Educational/Research Use: Security issues in clearly educational contexts
Theoretical Vulnerabilities: Issues without practical exploitation potential
Social Engineering: Non-technical attacks on users
Physical Security: Physical access to deployment infrastructure
Third-Party Services: Security issues in external services (HF Spaces, Supabase)

Rate Limiting

API Abuse: Automated tools hitting prediction endpoints
DoS Testing: Denial of service testing without prior approval
Load Testing: Excessive load testing on shared infrastructure

📚 Security Resources

Medical Security Standards

HIPAA Security Rule: Healthcare data protection requirements
HITECH Act: Enhanced healthcare security provisions
FDA Cybersecurity: Medical device security guidelines
NIST Cybersecurity Framework: General security best practices

Technical Security Resources

OWASP Top 10: Web application security risks
CWE/SANS Top 25: Most dangerous software errors
Python Security: Python-specific security best practices
ML Security: Machine learning security considerations

Training and Certification

Healthcare IT Security: Specialized medical security training
Python Security: Secure Python development practices
Web Application Security: General web security principles
Privacy Engineering: Data protection and privacy design

🛠️ Security Tools

Development Tools

# Static analysis
bandit -r .  # Python security linter
semgrep --config=auto .  # Multi-language security scanner

# Dependency checking
safety check  # Check for known vulnerabilities
pip-audit  # Alternative dependency checker

# Secret detection
detect-secrets scan --all-files  # Find secrets in code
git-secrets --scan  # Git hook for secret detection

Deployment Security

# Container security
docker scan your-image:latest  # Docker security scan
trivy image your-image:latest  # Vulnerability scanner

# Infrastructure security
terraform plan -out=plan.out  # Infrastructure as code security
checkov -f plan.out  # Terraform security scanner

📞 Security Contacts

Primary Security Contact

Email: security@raghav0079.dev
Response Time: 24 hours maximum
Escalation: For critical issues affecting patient safety

Security Team

Lead Developer: @Raghav0079
Medical Advisor: Available for medical security concerns
Infrastructure: Cloud security and deployment issues

Emergency Contact

For critical security issues affecting patient safety:

Immediate: security@raghav0079.dev with subject "CRITICAL MEDICAL SECURITY"
Follow-up: GitHub security advisory
Escalation: Direct contact via GitHub

📋 Security Changelog

Version 1.0.0 (Current)

✅ Implemented secure input validation
✅ Added HTTPS-only deployment
✅ Removed persistent data storage by default
✅ Added comprehensive security documentation
✅ Implemented rate limiting
✅ Added security headers for web deployment

Planned Security Enhancements

🔄 Multi-factor authentication for admin features
🔄 Advanced input sanitization
🔄 Comprehensive audit logging
🔄 Enhanced encryption for optional database features
🔄 Security compliance certifications

🏥 Medical Security Notice

Remember: This application is designed for educational and research purposes only. Any use with real patient data requires:

✅ Proper security assessment
✅ Healthcare compliance review
✅ Professional medical oversight
✅ Appropriate legal and regulatory compliance

Never use this tool for actual medical diagnosis or treatment decisions.

For questions about security or to report vulnerabilities, please contact: security@raghav0079.dev

FilesExpand file tree

SECURITY.md

Latest commit

History