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Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents — arXiv:2610.07258

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07258
License (as archived): CC0-1.0
Archived: 2026-10-07T11:20:27.808Z
SHA-256 of the archived text: c6ec3d3af3172ebe9f0edc04e20a7eb50f90af43613f862235c344fa1c80e6a7
Stable URL of this page: https://metraipolis.com/documento/dc_08cffdd2be79

How to cite

Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents — arXiv:2610.07258 — arXiv (en), https://arxiv.org/abs/2610.07258 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_08cffdd2be79 (Metraipolis archive dc_08cffdd2be79, sha256 c6ec3d3af3172ebe9f0edc04e20a7eb50f90af43613f862235c344fa1c80e6a7)

BibTeX

@misc{dc08cffdd2be79,
  title = {Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents — arXiv:2610.07258},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07258},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_08cffdd2be79; sha256 c6ec3d3af3172ebe9f0edc04e20a7eb50f90af43613f862235c344fa1c80e6a7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07258. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester could not derive, and departments can silently compute a same-named key performance indicator (KPI) through conflicting logic. Existing agent-memory systems (e.g., MemGPT, Zep, A-MEM) gate retrieval by content, ownership, and role, not derivation, missing a cached insight that embeds a forbidden column. We introduce the Analytical Memory Unit (AMU), a memory schema that attaches a full derivation (lineage) graph to every cached result, gated by a retrieval policy that serves a hit only when the requester is authorised for every column touched. Provided lineage recording is complete, we prove by construction that the policy blocks retrieval of results derived from a sensitive column outside the requester's permissions, at O(n) worst case -- a conditional design guarantee, not an empirical claim, that excludes derived features encoding sensitive information without naming their source. Eliminating measured leakage required 75-90% recorded lineage completeness, so we treat 90% as a conservative deployment target. Across six experiments, lineage-gated retrieval removes the 18.8-25.5% cross-department leakage naive content-gated memory suffers, keeping 81.5-82.6% of memory reuse at 13.8 microsecond worst-case overhead. A real-agent proof-of-concept with LLM-generated SQL is consistent with the guarantee: zero leaks over 9 round-trips, two conflicts caught automatically -- though a feasibility demonstration, not evidence of production viability. This offers a practical governance layer for shared agent memory, complementing source-layer access control and supporting EU AI Act compliance.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.07258 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:15:24.692Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).