DATDAT-002 — Data Privacy and Protection Failures
Personal Data Harvested as Default ML Training Input Without Consent Controls
4/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Machine learning systems routinely ingest location, identity, and behavioural trajectory data with no defined consent or minimisation framework. Boards face regulatory exposure under data protection law and reputational risk from opaque data practices embedded in core AI pipelines.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions (Saghiri2022) ↗https://airisk.mit.edu/
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