Data Governance

The set of policies, processes and controls governing data collection, quality, minimisation, provenance, storage, access controls and lifecycle management to ensure lawful, ethical and auditable use of data in AI systems. The Recommendations tie data governance requirements to privacy protections, documentation (data inventories) and procurement specifications.

Policies, technical infrastructures and processes to ensure high-quality, interoperable, securely stored, and ethically sourced data, including data catalogues, access policies and provenance tracking to support trustworthy AI development and reuse.

The set of policies, standards, infrastructures and practices for ensuring quality, availability, interoperability, sharing and responsible use of public and private data that underpin AI development and deployment, including data-sharing frameworks and access provisions described in the EBIA Annex.

Arrangements encompassing legal, organisational and technical measures that enable responsible data availability, quality, access, consent management and privacy‑preserving use across public and private sectors. The plan frames data governance as the foundation for interoperable data sharing, trustworthy data pipelines, and compliance with GDPR and related rules.

Operational rules and practices governing the management, sharing and reuse of data, emphasizing open data principles combined with privacy-preserving mechanisms and compliance with applicable data protection law to ensure lawful, secure and ethical data use across public sector projects.

Principles and practices to make data findable, accessible, interoperable and reusable (FAIR) for innovation while ensuring lawful processing and privacy safeguards; includes use of MyData principles, documentation of lawful bases, and processes like DPIAs to manage risk.

Policies, roles and processes established to ensure data quality, stewardship, interoperability and reuse across public administrations, including enterprise data governance models, data quality metrics, open data provisions and responsibilities for data stewardship and cataloguing.

A framework covering lawful processing, consent management, data minimization, retention policies, quality controls, and cross-sector stewardship to ensure privacy-respecting, secure, and appropriate use of data in AI development and deployment.

Institutional and technical arrangements for the collection, classification, storage, sharing and protection of government and public-sector data, including metadata catalogs, shared platforms and rules for administrative data use.

Rules and technical controls governing the collection, use, sharing, quality, and provenance of datasets used in AI systems, including privacy-preserving practices and compliance with data protection laws. NAIO emphasizes data governance to support secure dataset consolidation, public data stewardship, and lawful cross-sector data use.

The technical, legal, and institutional arrangements for interoperable data architectures, secure data sharing, and stewardship of datasets used for AI, implemented consistent with the Philippines' Data Privacy Act (RA 10173) and NPC guidance. NAISR 1.0 frames data governance as enabling infrastructure for AI adoption and reuse while ensuring privacy and compliance.

The set of policies, procedures, standards, and responsibilities governing the collection, storage, sharing, protection, and use of data to ensure privacy, security, quality, and appropriate access for AI systems. In this policy, data governance frames how data supporting AI should be managed consistent with Rwanda's Data Protection and Privacy Law (Law No. 058/2021).

Frameworks, policies, and practices for managing data quality, accessibility, interoperability, privacy, and security across sectors. NAISR 2.0 prioritizes data governance to balance open-data initiatives, data marketplaces, and protection of sensitive information to enable responsible AI value extraction.

The policies and practices governing how data used by AI systems is collected, stored, shared, secured, and made available, emphasizing data minimization, purpose limitation, data protection by design and by default, mechanisms for open and shared non-sensitive data, and measures to ensure data quality and integrity for trustworthy AI operation.

The set of policies, standards, roles and technical measures that ensure data quality, interoperability, privacy protection, stewardship, access controls and, where applicable, data sovereignty; intended to guide responsible data sharing, open data practices and compliance across public sector AI initiatives.

The policies and stewardship arrangements governing data access, sharing, privacy protections, and security, including mechanisms to enable appropriate data sharing for public-interest projects while aligning with privacy, national security, and ethical priorities. The Document positions data governance as a cross-cutting layer to be operationalized by implementing agencies.

The set of policies, processes and record-keeping practices that govern the collection, storage, access, provenance, use, retention and auditing of datasets and models used in journalistic and editorial AI workflows, intended to protect privacy, ensure accountability, and support complaints-handling.

An operational concept encompassing the structures, roles, policies, and processes that enable secure, lawful, and value-creating data sharing and interoperability across public and private actors. In the strategy it underpins recommendations for privacy, data exchange, and national data management.

Defined as the set of policies, technical controls, standards and agreements that enable secure, privacy-preserving sharing, access, storage and reuse of data across public and private entities, including anonymization, security controls and data-sharing agreements.

Data Governance refers to the overall management of the availability, usability, integrity, and security of data used within AI systems and the broader digital ecosystem. This includes establishing policies, standards, and procedures to ensure data quality, privacy, and ethical handling, especially for data generated by Basque industry.

Data governance refers to robust systems that ensure data protection, privacy, quality, and secure sharing for AI development. It is highlighted in relation to the need for robust systems that ensure data protection, privacy, quality, and secure sharing for AI development.