This Security Policy outlines the security practices, guidelines, and procedures for the FinancialEconomic repository. Given the sensitive nature of financial calculations and economic modeling, we prioritize security, data integrity, and responsible disclosure of vulnerabilities.

We currently support the following versions with security updates:

Note: This is an educational and research repository. For production use, always use the latest version and conduct thorough security audits.

We take security vulnerabilities seriously. If you discover a security issue, please follow responsible disclosure practices:

1. DO NOT open a public GitHub issue for security vulnerabilities
2. Send an email to the repository maintainers with:
   • Detailed description of the vulnerability
   • Steps to reproduce the issue
   • Potential impact assessment
   • Suggested fixes (if any)
3. Use the subject line: [SECURITY] Vulnerability Report - [Brief Description]

• Acknowledgment: Within 48 hours of submission
• Initial Assessment: Within 5 business days
• Status Updates: Regular updates on remediation progress
• Resolution: We aim to resolve critical vulnerabilities within 30 days
• Credit: Security researchers who responsibly disclose vulnerabilities will be credited (unless they prefer to remain anonymous)

• Input Validation: Always validate and sanitize user inputs
• No Hardcoded Secrets: Never commit API keys, passwords, or sensitive credentials
• Secure Dependencies: Keep dependencies up-to-date and audit for known vulnerabilities
• Code Review: All changes must go through code review before merging
• Least Privilege: Request only necessary permissions and access

• Precision: Use appropriate data types (e.g., Decimal for monetary values)
• Validation: Validate all financial inputs and calculations
• Logging: Log important operations without exposing sensitive data
• Error Handling: Implement proper error handling to prevent information leakage
• Overflow Protection: Guard against arithmetic overflow in calculations

• No Real Data: Do not commit real financial data or personally identifiable information (PII)
• Test Data: Use synthetic or anonymized data for testing
• Data Minimization: Only collect and process necessary data
• Secure Storage: If data must be stored, use encryption at rest

• Virtual Environments: Always use virtual environments (venv, conda)
• Dependency Verification: Verify package integrity before installation
• Regular Updates: Keep Python and all dependencies updated
• Secure Configuration: Review and secure any configuration files

• Source Review: Review code before executing financial calculations
• Isolated Testing: Test in isolated environments
• Backup Data: Maintain backups before running financial operations
• Audit Trail: Keep logs of important calculations and decisions

We recommend regular security audits of dependencies:

# Check for known vulnerabilities
pip install safety
safety check --file requirements.txt

# Alternative: using pip-audit
pip install pip-audit
pip-audit

• Review dependencies monthly for security updates
• Test updates in a separate branch before merging
• Document any breaking changes
• Pin critical dependency versions in requirements.txt

Dependencies with potential security implications:

• TensorFlow/Keras: ML frameworks - keep updated for security patches
• Flask: Web framework - ensure proper security headers and CSRF protection
• NumPy/Pandas: Data processing - validate inputs to prevent injection attacks
• Requests: HTTP library - verify SSL certificates, use timeouts

• Principle of Least Privilege: Contributors receive minimum necessary permissions
• Branch Protection: Main branch requires pull request reviews
• Two-Factor Authentication: Required for all contributors
• Access Review: Regular review of contributor access

• Environment Variables: Use environment variables for sensitive configuration
• GitHub Secrets: Store CI/CD secrets in GitHub Secrets
• No Commits: Never commit secrets to version control
• Rotation: Regularly rotate API keys and credentials

# ❌ Bad: No validation
def calculate_interest(principal, rate, time):
    return principal * rate * time

# ✅ Good: With validation
def calculate_interest(principal, rate, time):
    if not isinstance(principal, (int, float)) or principal <= 0:
        raise ValueError("Principal must be a positive number")
    if not isinstance(rate, (int, float)) or rate < 0:
        raise ValueError("Rate must be a non-negative number")
    if not isinstance(time, (int, float)) or time <= 0:
        raise ValueError("Time must be a positive number")
    return principal * rate * time

from decimal import Decimal, getcontext

# Set precision for financial calculations
getcontext().prec = 28

def calculate_compound_interest(principal, rate, time, compounds_per_year):
    """Calculate compound interest with proper decimal precision."""
    principal = Decimal(str(principal))
    rate = Decimal(str(rate))
    time = Decimal(str(time))
    compounds = Decimal(str(compounds_per_year))
    
    return principal * (1 + rate / compounds) ** (compounds * time)

# ❌ Bad: Exposes internal details
def load_portfolio(file_path):
    return pd.read_csv(file_path)

# ✅ Good: Secure error handling
def load_portfolio(file_path):
    try:
        if not os.path.exists(file_path):
            raise FileNotFoundError("Portfolio file not found")
        return pd.read_csv(file_path)
    except Exception as e:
        logger.error("Error loading portfolio", exc_info=True)
        raise ValueError("Unable to load portfolio data") from None

Before submitting code, ensure:

• No hardcoded credentials or API keys
• All inputs are validated and sanitized
• Appropriate error handling is implemented
• No sensitive data in logs or error messages
• Dependencies are up-to-date and secure
• Code follows secure coding guidelines
• Financial calculations use appropriate precision
• No SQL injection or code injection vulnerabilities
• Proper authentication and authorization checks

1. Detection: Identify and confirm the security incident
2. Containment: Limit the scope and impact of the incident
3. Investigation: Analyze the root cause and extent of the breach
4. Remediation: Fix vulnerabilities and deploy patches
5. Recovery: Restore normal operations and verify security
6. Post-Incident: Document lessons learned and update procedures

• Internal: Notify maintainers immediately
• Users: Inform users if their data or security is affected
• Transparency: Publish post-mortem reports for significant incidents
• Timeline: Maintain clear timeline of events

This repository is for educational and research purposes only:

• Not intended as financial advice
• No warranty for accuracy of calculations
• Users are responsible for validating results
• Not suitable for production financial systems without thorough auditing

• Respect all open-source licenses
• Attribute third-party code appropriately
• Do not include proprietary or copyrighted code without permission

• OWASP Top 10: https://owasp.org/www-project-top-ten/
• Python Security Best Practices: https://python.readthedocs.io/en/stable/library/security_warnings.html
• CWE Database: https://cwe.mitre.org/
• CVE Database: https://cve.mitre.org/

• PEP 668: Secure Python Installation
• NIST Cybersecurity Framework
• GDPR Compliance Guidelines (for data handling)
• Financial Industry Security Standards

This security policy is reviewed and updated:

• Regular Review: Quarterly
• Incident-Based: After security incidents
• Community Feedback: Based on security researcher input
• Industry Standards: When security standards evolve

Last Updated: January 2026

For security concerns or questions about this policy:

• Open a general discussion (non-security issues only)
• Contact repository maintainers directly for security issues
• Follow responsible disclosure guidelines

Remember: Security is everyone's responsibility. Thank you for helping keep this project secure!