Data

Encompasses public and private data assets, including open public datasets, standardised metadata, and governance models for controlled access to private sector data. The Plan highlights interoperable, open data standards and alignment with existing data protection law for responsible use.

The statute adopts the EU AI Act terminology but expressly expands the notion of 'data' at the national level to cover a wide range of inputs and assets used across the AI lifecycle, explicitly including audio and visual recordings; this definition governs data used for training, validation, inputs and outputs of AI models where the national text applies.

Includes both personal and non‑personal datasets used throughout the AI lifecycle (training, validation, testing, and operation), and highlights concerns around data quality, provenance and applicability of data protection obligations such as the PDPA when personal data are involved.

Any record of information in electronic or other forms, covering a broad range of factual or digital records regardless of format. The DSL uses this expansive definition as the baseline for scope, obligations and classification under the law.

Any information, files, records or content that are processed, stored, transmitted or received via computer or telecommunications systems; the statute uses this term as the basic object of protection and further distinguishes subcategories by sensitivity. The law’s definition frames offences (e.g., unauthorized access, disclosure) and applicable penalties.

Data is read broadly to include numeric, alphabetic, photographic, acoustic or other formats linked to identifiable persons (consistent with Law N°19.628), and explicitly covers behavioral, preference and inferred consumer information collected or produced by AI systems.

Defined broadly to include datasets generated by the public sector, industrial sensors, healthcare records, and other domain sources that enable AI training and inference; the Strategy frames data availability, sharing frameworks, and privacy-protecting data pipes as essential infrastructure for AI social implementation. It highlights both the value of such data for algorithm development and the need for governance, privacy safeguards, and platform specifications.

Data refers to the raw facts, figures, and statistics that AI systems process and learn from to identify patterns, make predictions, or generate outputs. The quality and representativeness of data are crucial for the fairness and reliability of AI systems.