mlsanityData to 14 Oct 2026

The Synthetic Mirror -- Synthetic Data at the Age of Agentic AI

arXiv 2506.13818 ↗First posted 15 Jun 2025cs.CY cs.LGcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Synthetic data, which is artificially generated and intelligently mimicking or supplementing the real-world data, is increasingly used. The proliferation of AI agents and the adoption of synthetic data create a synthetic mirror that conceptualizes a representation and potential distortion of reality, thus generating trust and accountability deficits. This paper explores the implications for privacy and policymaking stemming from synthetic data generation, and the urgent need for new policy instruments and legal framework adaptation to ensure appropriate levels of trust and accountability for AI agents relying on synthetic data. Rather than creating entirely new policy or legal regimes, the most practical approach involves targeted amendments to existing frameworks, recognizing synthetic data as a distinct regulatory category with unique characteristics.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 28 of its 63 references that are in the map. First posted on arXiv 15 Jun 2025.

Builds on

28 of 63 references.

References · 28

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16 more
  1. 2025APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplaycited by 167
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  4. 2024Comprehensive Exploration of Synthetic Data Generation: A Surveycited by 60
  5. 2024Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMscited by 50
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  16. 2023Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environmentcited by 4

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  1. 2025Rethinking Data Protection in the (Generative) Artificial Intelligence Era
  2. 2026Security, privacy, and agentic AI in a regulatory view: From definitions and distinctions to provisions and reflections
  3. 2023Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective
  4. 2024Best Practices and Lessons Learned on Synthetic Data
  5. 2026Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data
  6. 2026Privilege and confidentiality in generative AI workflows
  7. 2023Regulating ChatGPT and other Large Generative AI Models
  8. 2026Transparency as Architecture: Structural Compliance Gaps in EU AI Act Article 50 II