DATDAT-002 — Data Privacy and Protection Failures
Confidential Data Ingested During Model Training
3/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Sensitive or proprietary information risks being embedded into AI models when training data is not properly screened. Organisations face regulatory exposure and loss of competitive confidentiality if such models are deployed or shared externally.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — AI Risk Atlas (IBM2025) ↗https://airisk.mit.edu/
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