The Valquiria Data Analysis Suite is a research platform for physiological data analysis. We take security seriously, particularly regarding:

• Data Privacy: Protection of physiological data and research participants
• Research Integrity: Preventing unauthorized access or modification of research data
• Code Security: Ensuring the analysis platform is free from vulnerabilities

This software is intended for RESEARCH PURPOSES ONLY

• ❌ NOT APPROVED for operational military systems
• ❌ NOT APPROVED for clinical diagnosis or treatment
• ❌ NOT APPROVED for safety-critical applications
• ✅ ONLY for research, education, and scientific analysis

Using this software in operational or clinical environments may pose serious security and safety risks.

• No Real Data in Repository: We never store actual physiological data in the codebase
• Encryption Support: Analysis supports encrypted data storage
• Access Controls: Configurable data access permissions
• Audit Logging: Optional logging of data access and analysis operations

• Input Validation: Comprehensive validation of all data inputs
• Dependency Scanning: Regular scanning for vulnerable dependencies
• Static Analysis: Code security analysis with Bandit
• Sandboxed Execution: Analysis runs in isolated environments when possible

• Anonymization: Tools for data anonymization and de-identification
• GDPR Compliance: Support for data subject rights and privacy regulations
• Data Minimization: Only processes data necessary for analysis
• Secure Deletion: Secure cleanup of temporary data files

DO NOT create public GitHub issues for security vulnerabilities.

Instead, please:

1. Email: [security contact - to be provided]
2. Include:
   • Detailed description of the vulnerability
   • Steps to reproduce the issue
   • Potential impact assessment
   • Your assessment of severity
   • Suggested mitigation if known

If you discover issues related to data privacy or participant confidentiality:

1. Immediate Contact: Dr. Diego Malpica - dlmalpicah@unal.edu.co
2. Include:
   • Nature of the privacy concern
   • Affected data or participants (if known)
   • Potential exposure or risk
   • Recommended immediate actions

• Acknowledgment: Within 24 hours
• Initial Assessment: Within 72 hours
• Status Update: Weekly until resolved
• Fix Timeline:
  • Critical: 1-7 days
  • High: 2-4 weeks
  • Medium: 1-3 months
  • Low: Next major release

• Remote code execution
• Data exfiltration or exposure
• Authentication bypass
• Privilege escalation
• Data corruption or loss

• Local privilege escalation
• Information disclosure
• Cross-site scripting (if applicable)
• SQL injection (if applicable)
• Denial of service attacks

• Data validation issues
• Authorization flaws
• Configuration security issues
• Dependency vulnerabilities

• Information leakage
• Minor input validation issues
• Non-exploitable crashes

1. Virtual Environment: Always use isolated Python environments
2. Regular Updates: Keep dependencies updated
3. Data Security: Encrypt sensitive physiological data
4. Access Control: Limit access to analysis systems
5. Backup Security: Secure backup of research data
6. Network Security: Use secure networks for data transfer

1. Code Review: All code changes require security review
2. Input Validation: Validate all external inputs
3. Secrets Management: Never commit secrets or credentials
4. Dependency Management: Regular security updates
5. Testing: Include security test cases
6. Documentation: Document security considerations

1. IRB Compliance: Follow institutional review board requirements
2. Data Governance: Implement data governance policies
3. Participant Consent: Ensure proper consent for data use
4. Data Retention: Follow data retention and deletion policies
5. Publication Ethics: Protect participant privacy in publications

• Dependency Scanning: GitHub Dependabot alerts
• Code Analysis: Bandit for Python security analysis
• Container Scanning: Docker image security scanning (if applicable)
• License Compliance: Automated license checking

# Install security tools
pip install bandit safety

# Run security analysis
bandit -r src/
safety check

# Check for known vulnerabilities
pip-audit

• Automated security scanning in GitHub Actions
• Pull request security reviews
• Dependency vulnerability alerts
• Code signing for releases (planned)

• Dependency vulnerabilities
• Code security issues
• Access patterns (if logging enabled)
• Data integrity checks
• System resource usage

1. Detection: Automated alerts and user reports
2. Assessment: Severity classification and impact analysis
3. Containment: Immediate mitigation measures
4. Investigation: Root cause analysis
5. Resolution: Permanent fix implementation
6. Communication: Stakeholder notification
7. Documentation: Incident documentation and lessons learned

While this is a research platform, if used with data subject to HIPAA:

• Administrative Safeguards: Access controls and workforce training
• Physical Safeguards: Secure workstations and media controls
• Technical Safeguards: Access control, audit controls, integrity, person authentication

• GDPR (EU): Data subject rights and privacy by design
• PIPEDA (Canada): Personal information protection
• Privacy Act (Australia): Health information privacy
• Other: Consult local regulations for your jurisdiction

• OWASP Top 10
• NIST Cybersecurity Framework
• Research Data Security Guidelines

• Secure coding practices
• Data privacy regulations
• Incident response procedures
• Research ethics and data handling

• Primary: [To be provided]
• Secondary: Dr. Diego Malpica - dlmalpicah@unal.edu.co

• FAC Research Board: [To be provided]
• Institutional IRB: [To be provided]

• Data Protection Officer: [To be provided]
• Privacy Contact: [To be provided]

We appreciate security researchers who responsibly disclose vulnerabilities. We commit to:

• Acknowledging receipt of vulnerability reports
• Working with researchers to understand and validate issues
• Providing regular updates on fix progress
• Crediting researchers in security advisories (with permission)
• Not pursuing legal action against responsible security research

Thank you for helping keep the Valquiria research community and participant data secure! 🛡️