DAVE: A Policy-Enforcing LLM Spokesperson for Secure Multi-Document Data Sharing
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Abstract (arXiv)
In current inter-organizational data spaces, usage policies are enforced mainly at the asset level: a whole document or dataset is either shared or withheld. When only parts of a document are sensitive, providers who want to avoid leaking protected information typically must manually redact documents before sharing them, which is costly, coarse-grained, and hard to maintain as policies or partners change. We present DAVE, a usage policy-enforcing LLM spokesperson that answers questions over private documents on behalf of a data provider. Instead of releasing documents, the provider exposes a natural language interface whose responses are constrained by machine-readable usage policies. We formalize policy-violating information disclosure in this setting, drawing on usage control and information flow security, and introduce virtual redaction: suppressing sensitive information at query time without modifying source documents. We describe an architecture for integrating such a spokesperson with Eclipse Dataspace Components and ODRL-style policies, and outline an initial provider-side integration prototype in which QA requests are routed through a spokesperson service instead of triggering raw document transfer. Our contribution is primarily architectural: we do not yet implement or empirically evaluate the full enforcement pipeline. We therefore outline an evaluation methodology to assess security, utility, and performance trade-offs under benign and adversarial querying as a basis for future empirical work on systematically governed LLM access to multi-party data spaces.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 26 references that are in the map. First posted on arXiv 19 Feb 2026.
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6 of 26 references.
References · 6
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
- 2017Selective Classification for Deep Neural Networkscited by 799
- 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
- 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 549
- 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
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