Dataspilldesign

Dataspilldesign is an engineering practice focused on preventing unintended data disclosure by the deliberate design of information systems and data flows. It emphasizes reducing the chance that sensitive information is exposed during collection, processing, storage, or transmission.

Practitioners work on data discovery and classification, access control, data minimization, and secure-by-design principles. They aim to implement least-privilege policies, segmentation of data repositories, and clear ownership to ensure accountability in how data moves through pipelines such as ETL processes and data lakes.

Common techniques include data loss prevention technologies, encryption at rest and in transit, data masking and tokenization, pseudonymization, and the use of differential privacy where appropriate. Monitoring, logging, and regular audits support detection of potential spills and aid incident response.

Governance aspects cover risk assessment, policy development, and data stewardship, with alignment to privacy and protection regulations (for example GDPR, HIPAA, or regional equivalents). Design reviews, threat modeling, and privacy-by-design practices are integrated into development lifecycles.

Relation and reception: The term is not universally standardized and can overlap with data protection, privacy engineering, and data governance. In practice, dataspilldesign is often described as an approach within an organization's broader security and privacy program.