Research Summary: Data Governance for Generative and Agentic AI : Technology, Law, Economics, and Security

A newly announced research volume establishes a comprehensive, unified data governance framework designed for generative AI and autonomous multi-agent systems. Integrating technological, legal, and economic lenses, the work outlines end-to-end lifecycle governance encompassing retrieval-augmented generation, data provenance, zero-trust security, cross-jurisdictional regulation across six regimes, and societal applications.

Why it matters

As generative tools evolve into autonomous agentic systems that actively act on data, fragmented risk management exposes enterprises to multi-jurisdictional compliance failures and systemic security vulnerabilities. Establishing an integrated governance framework spanning technical controls, legal mandates, and economic models is critical for deploying reliable agentic workflows at scale.

Key insights

• Proposes a unified lifecycle governance framework addressing systems that retrieve, generate, infer, remember, and act upon information.
• Synthesizes technological, legal, and economic perspectives across eleven chapters and eight appendices.
• Covers technical and operational domains including data lineage, provenance, digital trust, cybersecurity, and zero-trust architectures.
• Examines regulatory and societal dimensions, including intellectual property, global data protection laws, comparative regulation across six jurisdictions, and election integrity.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2610.33605