DATDAT-002 — Data Privacy and Protection Failures
Generative AI Models Leak Sensitive Personal Data from Training Sets
3/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models retain and expose private information through both deliberate extraction and inadvertent leakage during inference. Boards face regulatory liability under data protection law if training data governance and sanitisation controls are not formally established.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Mapping the Ethics of Generative AI: A Comprehensive Scoping Review (Hagendorff2024) ↗https://airisk.mit.edu/
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