Policy Statement
Cytognosis Foundation is committed to the highest standards of data governance, ensuring that all data collection, processing, storage, and sharing activities support our mission to transform healthcare through AI-driven preventive technologies while maintaining strict privacy protections, regulatory compliance, and ethical standards.
Scope and Applicability
This policy applies to all:
Personnel: Employees, contractors, volunteers, and affiliates
Data Types: All health data, research data, operational data, and metadata
Systems: All technology platforms, databases, and data processing systems
Partners: Collaborating institutions, vendors, and service providers
Locations: All facilities and remote work environments
Governance Structure
Data Governance Committee
Chair: Chief Data Officer (Shahin Mohammadi -
)
Voting Members:
Chief Executive Officer
Chief Technology Officer
Privacy Officer
Compliance Officer
Research Director
Legal Counsel
Advisory Members:
External Ethics Expert
Patient Advocate Representative
Regulatory Affairs Specialist
Information Security Officer
Roles and Responsibilities
Chief Data Officer (CDO)
Strategic Leadership: Overall data strategy and governance oversight
Policy Development: Create and maintain data governance policies
Risk Management: Identify and mitigate data-related risks
Compliance Oversight: Ensure regulatory and ethical compliance
Stakeholder Engagement: Coordinate with internal and external stakeholders
Privacy Officer
Privacy Protection: Implement and monitor privacy safeguards
Risk Assessment: Conduct privacy impact assessments
Incident Response: Lead privacy breach investigations and response
Training: Develop and deliver privacy training programs
Regulatory Liaison: Interface with privacy regulators and authorities
Compliance Officer
Regulatory Compliance: Monitor and ensure adherence to all regulations
Audit Management: Coordinate internal and external audits
Policy Implementation: Ensure policies are properly implemented
Reporting: Prepare compliance reports for leadership and regulators
Vendor Management: Oversee compliance aspects of vendor relationships
Research Director
Scientific Integrity: Ensure research data quality and validity
Ethics Oversight: Coordinate with IRBs and ethics committees
Collaboration Management: Oversee research partnerships and data sharing
Publication Standards: Ensure responsible research publication practices
Innovation Balance: Balance innovation needs with governance requirements
Data Classification Framework
Classification Levels
Level 1: Public Data
Definition: Data approved for public release and sharing
Examples: Published research results, anonymized datasets, public reports
Controls: Standard attribution and usage guidelines
Access: Unrestricted public access
Retention: Permanent retention for research value
Level 2: Internal Data
Definition: Data for internal organizational use only
Examples: Operational metrics, internal reports, strategic plans
Controls: Employee access controls, confidentiality agreements
Access: Authorized personnel only
Retention: Business need-based retention periods
Level 3: Confidential Data
Definition: Sensitive data requiring special protection
Examples: Research protocols, partnership agreements, financial data
Controls: Role-based access, encryption, audit logging
Access: Need-to-know basis with approval
Retention: Legal and regulatory requirements
Level 4: Restricted Data (PHI/PII)
Definition: Protected health information and personally identifiable information
Examples: Patient records, genetic data, clinical trial data
Controls: Maximum security measures, HIPAA compliance
Access: Strictly controlled with documented justification
Retention: Minimum necessary periods with secure disposal
Data Handling Requirements
Collection & Acquisition
Lawful Basis: Establish legal basis for all data collection (HIPAA/GDPR compliance).
Consent Management: Obtain and manage appropriate patient consents prior to collection.
External Data Acquisition: All requests for external controlled-access data (e.g., NIH NDA, PEC, NBB) must strictly adhere to the procedures outlined in the Controlled Data Access Policy, requiring formal Data Access Requests (DAR) and Data Use Certifications (DUC).
Minimization: Collect only data necessary for stated purposes.
Transparency: Provide clear information about data collection models.
Processing
Purpose Limitation: Process data only for stated, legitimate research purposes.
DPIA Triggers: A formal Data Protection Impact Assessment (DPIA) must be triggered prior to any new high-risk processing of sensitive PHI, massive-scale data ingestion, or deployment of novel AI training models.
Accuracy: Maintain data accuracy, provenance tracking, and completeness.
FDA SaMD Readiness: Processing pipelines training models intended for clinical intervention must be documented in compliance with FDA Software as a Medical Device (SaMD) quality frameworks, ensuring traceable clinical validation.
Storage
Encryption: Encrypt all sensitive data at rest (AES-256) and in transit (TLS 1.3).
Access Controls: Implement strict role-based access, explicitly logging all requests to Restricted Data (Level 4).
Backup: Maintain secure, tested, immutable backup systems in the appropriate GCP project — cytognosis-phi-prod for PHI workloads, cytognosis-data for regulated derivatives, cytognosis-infrastructure for non-PHI operations (see ../TECHNICAL_DATA_INFRASTRUCTURE.md).
Geographic Controls: Comply with data residency and cross-border transfer requirements.
Sharing
Authorization: Obtain approval from the Data Governance Committee for data sharing.
Agreements: Execute appropriate Data Use Agreements (DUA) (see Data Use Agreement Template).
De-identification: Apply privacy-preserving techniques (k-anonymity, differential privacy) before sharing.
Breach Response: Implement 72-hour breach notification procedures.
Privacy and Security Requirements
Privacy by Design Principles
1. Proactive not Reactive: Anticipate and prevent privacy invasions
2. Privacy as the Default: Maximum privacy protection without action
3. Full Functionality: Accommodate all legitimate interests without trade-offs
4. End-to-End Security: Secure data throughout its lifecycle
5. Visibility and Transparency: Ensure all stakeholders can verify practices
6. Respect for User Privacy: Keep user interests paramount
Technical Safeguards
Encryption: AES-256 encryption for data at rest, TLS 1.3 for data in transit
Access Controls: Multi-factor authentication and role-based access
Audit Logging: Comprehensive logging of all data access and modifications
Network Security: Firewalls, intrusion detection, and network segmentation
Vulnerability Management: Regular security assessments and patch management
Organizational Safeguards
Training: Regular privacy and security training for all personnel
Policies: Comprehensive policies covering all aspects of data handling
Incident Response: Documented procedures for security and privacy incidents
Vendor Management: Due diligence and ongoing monitoring of vendors
Physical Security: Appropriate physical controls for facilities and equipment
Regulatory Compliance Framework
United States Regulations
HIPAA: Health Insurance Portability and Accountability Act
HITECH: Health Information Technology for Economic and Clinical Health Act
FDA: Food and Drug Administration regulations for medical devices
FTC: Federal Trade Commission privacy and security requirements
State Laws: California CCPA, New York SHIELD Act, and other state requirements
International Regulations
GDPR: European Union General Data Protection Regulation
PIPEDA: Canadian Personal Information Protection and Electronic Documents Act
LGPD: Brazilian Lei Geral de Proteção de Dados
PDPA: Singapore Personal Data Protection Act
Country-Specific: Compliance with local regulations in all operating jurisdictions
Healthcare-Specific Standards
ISO 27799: Health informatics security management
ISO 13485: Medical devices quality management systems
ICH GCP: International Council for Harmonisation Good Clinical Practice
21 CFR Part 11: FDA electronic records and electronic signatures
CDISC: Clinical Data Interchange Standards Consortium
Data Quality Management
Quality Dimensions
Accuracy: Data correctly represents the real-world entity or event
Completeness: All required data elements are present
Consistency: Data is uniform across systems and time periods
Timeliness: Data is available when needed and up-to-date
Validity: Data conforms to defined formats and business rules
Uniqueness: No inappropriate duplication of data entities
Quality Assurance Processes
Data Profiling: Regular analysis of data quality metrics
Validation Rules: Automated checks for data quality at entry and processing
Cleansing Procedures: Standardized processes for correcting data quality issues
Monitoring: Continuous monitoring of data quality indicators
Reporting: Regular reporting on data quality metrics to stakeholders
Risk Management
Risk Assessment Framework
Risk Identification: Systematic identification of data-related risks
Risk Analysis: Assessment of likelihood and impact of identified risks
Risk Evaluation: Determination of risk tolerance and acceptance criteria
Risk Treatment: Implementation of controls to mitigate unacceptable risks
Risk Monitoring: Ongoing monitoring and review of risk landscape
Key Risk Categories
Privacy Risks: Unauthorized disclosure or re-identification of sensitive data
Security Risks: Data breaches, cyberattacks, and system vulnerabilities
Compliance Risks: Violations of regulatory requirements and legal obligations
Operational Risks: System failures, human errors, and process breakdowns
Reputational Risks: Damage to organizational reputation and stakeholder trust
Incident Management
Incident Classification
Level 1 - Critical: Immediate threat to patient safety or major data breach
Level 2 - High: Significant privacy or security incident requiring urgent response
Level 3 - Medium: Moderate incident with potential for escalation
Level 4 - Low: Minor incident with limited impact
Response Procedures
1. Detection and Reporting: Immediate reporting of suspected incidents
2. Assessment: Rapid assessment of incident scope and impact
3. Containment: Immediate actions to prevent further damage
4. Investigation: Thorough investigation to determine root cause
5. Remediation: Implementation of corrective and preventive actions
6. Communication: Appropriate notification of stakeholders and authorities
7. Documentation: Comprehensive documentation of incident and response
8. Lessons Learned: Analysis and improvement of processes based on incidents
Training and Awareness
Training Requirements
New Employee Orientation: Data governance training within first 30 days
Annual Refresher: Mandatory annual training for all personnel
Role-Specific Training: Specialized training based on job responsibilities
Incident Response Training: Regular drills and scenario-based training
Vendor Training: Training requirements for vendors handling organizational data
Training Content
Policy Overview: Understanding of data governance policies and procedures
Regulatory Requirements: Relevant laws and regulations affecting data handling
Privacy Protection: Techniques for protecting privacy and preventing breaches
Security Practices: Best practices for data security and system protection
Incident Response: Procedures for reporting and responding to incidents
Monitoring and Audit
Continuous Monitoring
Access Monitoring: Real-time monitoring of data access and usage
Quality Monitoring: Ongoing assessment of data quality metrics
Compliance Monitoring: Regular checks for regulatory compliance
Performance Monitoring: Tracking of governance program effectiveness
Risk Monitoring: Continuous assessment of risk landscape changes
Audit Program
Internal Audits: Regular internal assessments of governance practices
External Audits: Independent third-party audits of critical systems and processes
Regulatory Audits: Cooperation with regulatory examinations and inspections
Vendor Audits: Assessment of vendor compliance with data governance requirements
Audit Follow-up: Systematic tracking and resolution of audit findings
Policy Maintenance
Review Schedule
Annual Review: Comprehensive annual review of all policies and procedures
Regulatory Updates: Updates in response to regulatory changes
Incident-Driven Updates: Updates based on lessons learned from incidents
Technology Updates: Updates to address new technologies and capabilities
Stakeholder Feedback: Updates based on feedback from stakeholders
Change Management
Change Requests: Formal process for requesting policy changes
Impact Assessment: Assessment of proposed changes on operations and compliance
Approval Process: Defined approval authority for different types of changes
Communication: Systematic communication of policy changes to affected parties
Training Updates: Updates to training materials and programs
Enforcement and Sanctions
Compliance Monitoring
Regular Assessments: Systematic assessment of compliance with policies
Reporting Mechanisms: Multiple channels for reporting compliance concerns
Investigation Procedures: Fair and thorough investigation of alleged violations
Documentation: Comprehensive documentation of compliance activities
Corrective Actions: Appropriate corrective actions for identified violations
Sanctions Framework
Progressive Discipline: Escalating sanctions based on severity and frequency
Immediate Actions: Immediate suspension of access for serious violations
Training Requirements: Mandatory additional training for certain violations
Performance Management: Integration with performance management processes
Legal Action: Referral to legal authorities for criminal violations
Policy Owner: Chief Data Officer, Cytognosis Foundation
Approved By: Board of Directors
Effective Date: September 2025 (initial); revised May 2026 to align with consolidated infrastructure documentation
Next Review: November 2026
Version: 1.1
Classification: Internal Use Only