What is the purpose of data integrity checks and how are they implemented?

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Data integrity checks focus on keeping data accurate and consistent throughout its lifecycle, ensuring that information remains trustworthy as it’s created, stored, and moved. They guard against corruption, accidental errors, and unauthorized changes by verifying that data meets expected rules and remains unchanged where it should stay the same.

Implemented techniques include validation rules that enforce correct formats, required fields, and valid value ranges at entry or during processing. Database constraints—such as primary keys, foreign keys, unique and check constraints, and not-null rules—enforce correct relationships and valid data types. Checksums or hash-based methods generate a small digest of data so any alteration during storage or transmission can be detected. Audit trails or logs record who changed data, what was changed, and when, providing traceability and a basis for recovery if integrity issues arise. Together, these tools help maintain data accuracy and consistency across systems.

Other options describe goals or actions that are related to security, performance, or data lifecycle management, but they do not address maintaining data correctness and trustworthiness in the same way.