Data Integrity

Data integrity is the discipline of preserving data accuracy, completeness, and consistency across all stages of its lifecycle. It encompasses techniques and controls that prevent, detect, and correct unauthorized modifications, corruption, or loss of data. Core mechanisms include: data validation and schema/constraint enforcement to prevent invalid states; preservation of referential, entity, and domain integrity to maintain correct relationships and valid values; checksums, cryptographic hashes, and digital signatures to detect tampering; immutable storage, versioning, and audit trails to enable traceability and recovery; data cleansing, reconciliation, and anomaly detection to address inconsistencies; and robust access control, authentication, authorization, and change-management processes. While encryption protects confidentiality, data integrity is primarily achieved through hashing, signatures, validation, and controlled change processes. Integrity is assessed via metrics such as validation success rates and error rates in data pipelines.