Data Flow Control: Data Safety Policies for AI Agents
Agents increasingly generate SQL, orchestrate pipelines, and automate data analysis on behalf of users. While recent work improves query correctness, correctness is not safety. A query may be semantically valid yet violate regulatory, privacy, or business constraints that govern how data may be combined and released. We argue that enforcing such constraints is fundamentally a data infrastructure problem. This paper introduces Data Flow Control (DFC), a framework to declaratively specify and guar
Record details
Published: 4 June 2026
Source: arXiv
Category: Research
Topics: Regulation · Privacy · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (4 June 2026), “Data Flow Control: Data Safety Policies for AI Agents,” evidence record 1458, https://ethics.ai/record/1458 (originally published by arXiv).
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