DATDAT-002 — Data Privacy and Protection Failures
Language Models Leaking Private and Sensitive Information from Training Data
4/5Sector: TechnologyGeography: GlobalStage: OperateIngested: —
Executive Summary
Language models can expose trade secrets, health data, and personal information embedded in or inferable from training data, causing harm even when used correctly. Boards face regulatory liability and reputational damage without robust data governance controls over model training and deployment.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Taxonomy of Risks posed by Language Models (Weidinger2022) ↗https://airisk.mit.edu/
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