{
  "id": 1458,
  "url": "https://arxiv.org/abs/2606.05679v1",
  "title": "Data Flow Control: Data Safety Policies for AI Agents",
  "summary": "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",
  "authors": "Charlie Summers, Eugene Wu",
  "category": "research",
  "topics": "regulation,privacy-surveillance,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-04T04:01:24.000Z",
  "fetched_at": "2026-07-14T14:15:17.102Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1458",
  "original_url": "https://arxiv.org/abs/2606.05679v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}