Data Governance

Overview

Unisia LLC is committed to responsible data governance practices that ensure the security, privacy, quality, and ethical use of data throughout our AI companion platform. This Data Governance Framework outlines our policies, procedures, and principles for managing data assets.

Data Governance Principles

Data Classification

We classify data based on sensitivity and apply appropriate controls:

Data Lifecycle Management

Data Collection

• Collection limited to necessary purposes with legal basis
• Clear consent mechanisms where required
• Documentation of data sources and collection methods
• Privacy impact assessments for new data collection

Data Storage

• Encrypted storage (AES-256 or equivalent)
• Geographic considerations for data residency requirements
• Regular backups with disaster recovery procedures
• Access logging and monitoring

Data Processing

• Processing aligned with stated purposes and legal requirements
• Data minimization in AI training and operations
• Anonymization and pseudonymization where appropriate
• Secure processing environments with access controls

Data Sharing

• Data sharing agreements with third parties
• Assessment of recipient's data protection capabilities
• Limitations on downstream use and disclosure
• Cross-border transfer safeguards (e.g., Standard Contractual Clauses)

Data Retention and Disposal

• Defined retention periods based on legal requirements and business needs
• Automated deletion processes where feasible
• Secure disposal methods (e.g., cryptographic erasure)
• Documentation of disposal activities

AI-Specific Data Governance

Training Data Management

• Curation of diverse, representative, and high-quality training datasets
• Documentation of data provenance and lineage
• Bias detection and mitigation in training data
• Proper licensing and rights management for training data
• Version control for datasets used in model training

Model Data

• Secure storage of model weights and configurations
• Version control and provenance tracking for AI models
• Documentation of model training processes and parameters
• Protection of proprietary model architectures

Inference and Interaction Data

• Logging of AI inputs and outputs for quality assurance
• Privacy-preserving techniques for sensitive interactions
• User control over conversation history and retention
• Aggregation and anonymization for analytics and improvement

Access Control and Authorization

• Role-Based Access Control (RBAC): Permissions assigned based on job functions
• Principle of Least Privilege: Minimum necessary access granted
• Multi-Factor Authentication: Required for accessing sensitive data
• Regular Access Reviews: Periodic validation of user permissions
• Segregation of Duties: Critical functions require multiple approvals
• Automated Provisioning/De-provisioning: Timely access management

Data Quality Assurance

• Data validation rules and integrity constraints
• Regular data quality audits and assessments
• Error detection and correction procedures
• Data profiling and cleansing processes
• Monitoring of data quality metrics
• User feedback mechanisms for data issues

Compliance and Legal Requirements

Our data governance framework ensures compliance with applicable regulations, including:

• GDPR: EU General Data Protection Regulation
• CCPA/CPRA: California Consumer Privacy Act and amendments
• COPPA: Children's Online Privacy Protection Act (for Merrill and youth-facing services)
• HIPAA: Health Insurance Portability and Accountability Act (where applicable)
• FERPA: Family Educational Rights and Privacy Act (for educational contexts)
• Industry-Specific Regulations: Sector-specific data protection requirements

Data Breach Response

We maintain a comprehensive data breach response plan:

• Detection: Continuous monitoring for security incidents
• Assessment: Rapid evaluation of scope and impact
• Containment: Immediate steps to limit damage
• Notification: Timely communication to affected parties and regulators as required
• Remediation: Correction of vulnerabilities and restoration of security
• Review: Post-incident analysis and improvement

Roles and Responsibilities

Training and Awareness

• Mandatory data governance training for all employees
• Role-specific training for data stewards and custodians
• Regular updates on policy changes and emerging risks
• Simulated phishing and security awareness exercises
• Resources and support for data governance questions

Monitoring and Auditing

• Continuous monitoring of data access and usage patterns
• Regular internal audits of data governance practices
• Third-party security and compliance audits
• Automated alerts for policy violations or anomalies
• Annual review and update of data governance framework

Continuous Improvement

Data governance is an ongoing process. We are committed to:

• Regularly reviewing and updating policies
• Incorporating lessons learned from incidents
• Adopting emerging best practices and technologies
• Engaging stakeholders for feedback and input
• Measuring and reporting on governance metrics

Contact Us

For questions about our data governance practices: