SECAI Core · phase 4 of 15
Data integrity
To safeguard the trustworthiness and accuracy of data throughout its lifecycle
The Explain card
- Plain English
- Data integrity means keeping data trustworthy and accurate across its whole lifecycle, so it is not altered, corrupted or tampered with without someone noticing.
- Example
- A team hashes each training dataset snapshot. When a checkpoint is rebuilt later, the hash mismatch reveals that someone quietly modified a few thousand labels.
- Why it matters
- Models are only as reliable as the data that shaped them. Without integrity controls, poisoning is invisible and you cannot prove which data produced which model.
- Hook
- Trust the data the way you trust a sealed evidence bag.
Where it sits in the deck
Phase 4: Data Integrity, Governance, and Provenance
Once you know what data is, learn the principles that keep it trustworthy, traceable, and minimized throughout its lifecycle.
