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.

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.

Proposes a unified lifecycle governance framework addressing systems that retrieve, generate, infer, remember, and act upon information.

Forward consideration, not a verified fact.