DATDAT-002 — Data Privacy and Protection Failures
Data Leakage Risks in AI Research and Development Pipelines
3/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Improper data handling, unauthorised access, and adversarial attacks in AI systems create material risk of personal and proprietary data exposure. Boards must ensure data governance frameworks explicitly address AI pipeline vulnerabilities or face regulatory and reputational liability.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — AI Safety Governance Framework (TC2602024) ↗https://airisk.mit.edu/
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