AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows

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[Submitted on 14 Jun 2026]

Title:AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows

View a PDF of the paper titled AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows, by Vimal Nakrani

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Abstract:AI systems are increasingly used to assist consequential decisions in regulated domains such as auditing, finance, and healthcare. This creates a recurring obligation: an organization must be able to reconstruct, after the fact, which evidence informed a given conclusion, and to show that the record of that reasoning was not altered. Existing tools address related but distinct problems - model observability, drift monitoring, governance reporting - and are built for the machine-learning engineer operating a system, not the reviewer who must trace one specific conclusion back to its supporting evidence. We present AuditWeave, a lightweight Python library, with no runtime dependencies, that records the steps of AI-assisted and data-transformation workflows into a single append-only, hash-chained ledger. A small, system-agnostic event vocabulary spans both retrieval-augmented generation (RAG) pipelines and tabular/lakehouse transformations, so a conclusion that draws on both can be traced end-to-end through one record. Within a sealed ledger, any modification, reordering, insertion, or deletion of events is detectable through chain verification. We describe the design and evaluate recording overhead, scalability, and tamper-detection correctness on the reference implementation. The integrity guarantees cost tens of microseconds per event, and, as the hash-chain construction implies, verification flagged every injected mutation across four mutation classes over 2,000 randomized trials.

Comments: 8 pages, 3 figures, open-source implementation at this http URL

Subjects:

Machine Learning (cs.LG); Software Engineering (cs.SE)

Cite as: arXiv:2607.09682 [cs.LG]

(or arXiv:2607.09682v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.09682

arXiv-issued DOI via DataCite

Submission history

From: Vimal Nakrani [view email] [v1] Sun, 14 Jun 2026 21:46:04 UTC (136 KB)

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