DATDAT-002 — Data Privacy and Protection Failures
General-purpose AI infers sensitive personal data from user inputs
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models can derive highly accurate private attributes from contextual user inputs, exposing individuals to manipulation, discrimination, and data protection breaches. Boards face regulatory liability and reputational harm where AI systems process inferred sensitive data without adequate transparency or consent controls.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024) ↗https://airisk.mit.edu/
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