{
  "annotation_id": "a538adc0-c07a-45c9-a499-ab1d82a16a84",
  "date_created": "2026-02-17T05:53:16.032000Z",
  "date_modified": "2026-02-17T05:53:16.032000Z",
  "file_hash": "a3e969874763f3c3ee6282f3b5ce1ff4a1c501e33570fe542146f8dcfce43345",
  "private": false,
  "record": {
    "abstract": "Computational data governance aims to make the enforcement of governance policies and legal obligations more efficient and reliable. Recent advances in natural language processing and agentic AI offer ways to improve how organizations share and use data. But many barriers remain. Today\u0027s tools require technical skills and multiple roles to discover, request, and query data. Automating data access using enterprise AI agents is limited by the means to discover and autonomously access distributed data. Current solutions either compromise governance or break agentic workflows through manual approvals. To close this gap, we introduce Data Product MCP integrated in a data product marketplace. This data marketplace, already in use at large enterprises, enables AI agents to find, request, and query enterprise data products while enforcing data contracts in real time without lowering governance standards. The system is built on the Model Context Protocol (MCP) and links the AI-driven marketplace with cloud platforms such as Snowflake, Databricks, and Google Cloud Platform. It supports semantic discovery of data products based on business context, automates access control by validating generated queries against approved business purposes using AI-driven checks, and enforces contracts in real time by blocking unauthorized queries before they run. We assessed the system with feedback from $n=16$ experts in data governance. Our qualitative evaluation demonstrates effectiveness through enterprise scenarios such as customer analytics. The findings suggest that Data Product MCP reduces the technical burden for data analysis without weakening governance, filling a key gap in enterprise AI adoption.",
    "arxiv_id": "2601.08687",
    "authors": [
      "Marco Tonnarelli",
      "Filippo Scaramuzza",
      "Simon Harrer",
      "Linus W. Dietz"
    ],
    "categories": [
      "cs.ET"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Data Product MCP: Chat with your Enterprise Data",
    "url": "https://arxiv.org/abs/2601.08687",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "f6340bb7-eac1-4b62-8029-aa90c3002b4f",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}