arXiv:2606.05679v1 Announce Type: cross Abstract: 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 guarantee policy enforcement over tuple-level data flo
As AI agents become increasingly autonomous and integrate deeper into enterprise data workflows, the critical need for robust data safety and policy enforcement mechanisms is accelerating.
This development addresses a fundamental vulnerability in AI agent deployment, ensuring that autonomous systems can operate within established regulatory, privacy, and business constraints, thereby unlocking wider adoption.
The introduction of Data Flow Control (DFC) provides a declarative framework to specify and guarantee tuple-level data policy enforcement, shifting responsibility for data safety from agent developers to infrastructure.
- · Enterprise AI adopters
- · Data governance solution providers
- · Regulatory compliance platforms
- · AI agents developers
- · Organizations with immature data governance
- · Generative AI models lacking safety guardrails
- · Legacy data security systems
Wider deployment and trust in AI agents across sensitive enterprise data environments will increase.
The development of standardized protocols and certifications for AI data safety will accelerate, becoming a competitive differentiator.
New legal precedents and compliance frameworks specifically for AI agent data handling will emerge, impacting international data sovereignty discussions.
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Read at arXiv cs.AI