Data Integrity
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
The assurance that data remains accurate, consistent, and unaltered through every stage of its lifecycle: storage, transfer, and processing.
What is Data Integrity?
Data integrity is the assurance that data remains accurate, consistent, and unaltered through its whole lifecycle, whether it is being stored, moved, or processed. Nothing gets corrupted, silently changed, or disconnected from the records it belongs to.
Integrity is a precondition for trusting any system built on the data. Regulators treat it the same way: financial and healthcare rules, and audits of AI systems, all assume the underlying records have not been tampered with.
Types of Data Integrity
- Physical integrity protects data against hardware failure, corruption in storage, and loss in transmission.
- Logical integrity enforces formats, constraints, and rules, so a date field cannot hold a name and a required value cannot be blank.
- Referential integrity keeps related records connected: an order cannot point to a customer who does not exist.
- Domain integrity restricts values to their permitted range, such as an age that cannot be negative.
Data Integrity vs Data Quality
Data integrity asks whether data has stayed accurate and unaltered through its lifecycle; data quality asks whether it is fit for a purpose. Integrity protects data from change and corruption; quality asks whether the data was ever good enough to begin with. A pristine, well-guarded database can still be full of stale, irrelevant records.
| Data Integrity | Data Quality | |
|---|---|---|
| Core question | Has the data stayed accurate and unaltered? | Is the data fit for its purpose? |
| Measured against | The data’s correct original state | The intended use |
| Typical failure | Corruption, unauthorized changes, broken references | Outdated, incomplete, or irrelevant records |
| Main remedies | Validation rules, access controls, checksums | Cleaning, enrichment, better collection |
Example of Data Integrity
A bank processes a customer’s transfer. Before committing it, the system checks that the debit and credit balance out, that the referenced account exists, and that the amount falls within valid bounds. If any check fails, the whole transaction rolls back.
Every change is also written to an append-only log that no one can edit afterward. When auditors later reconstruct an account’s history, the log and the balances agree – that agreement is data integrity in practice.
Related AI terms: Data Quality · Data Lineage · Data Versioning · Security · Compliance
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About