| --- |
| license: cc-by-4.0 |
| pretty_name: Lineage-Aware Memory Governance |
| tags: |
| - ai-agents |
| - agent-memory |
| - data-lineage |
| - access-control |
| - privacy |
| - governance |
| --- |
| |
| # Lineage-Aware Memory Governance |
|
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| Companion repository for the paper **"Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents"** (Venkata Sangaraju, Sudhir Vissa). The paper is accepted in IEEE Access, 2026. |
|
|
| - arXiv: https://arxiv.org/abs/2610.07258 |
| - IEEE Xplore (open access): https://ieeexplore.ieee.org/document/11677041 |
| - DOI: [10.1109/ACCESS.2026.3730363](https://doi.org/10.1109/ACCESS.2026.3730363) |
|
|
| ## Summary |
|
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| Shared memory for AI agents can leak sensitive data through cached results that were computed legitimately. The paper introduces the **Analytical Memory Unit (AMU)**, which attaches a full derivation (lineage) graph to every cached result. Retrieval is gated so that a cache hit is served only when the requester is authorised for every column the result touches. Across six experiments, lineage-gated retrieval removes the 18.8–25.5% cross-department leakage seen with naive content-gated memory. It keeps 81.5–82.6% of memory reuse, with 13.8 µs worst-case overhead. |
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| Benchmark data and an interactive demo are coming soon. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{sangaraju2026lineage, |
| title = {Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents}, |
| author = {Sangaraju, Venkata and Vissa, Sudhir}, |
| journal = {IEEE Access}, |
| year = {2026}, |
| doi = {10.1109/ACCESS.2026.3730363}, |
| eprint = {2610.07258}, |
| archivePrefix = {arXiv} |
| } |
| ``` |
|
|
