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

arXiv: https://arxiv.org/abs/2607.09682 · HTML: https://mlsanity.com/paper/2607.09682/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 6 references that are in the map.
- First posted on arXiv 14 Jun 2026.

## Builds on (most cited first, 6 of 6)

- [Audit Trails for Accountability in Large Language Models](https://mlsanity.com/paper/2601.20727/) (2026)
- [Provenance Tracking in Large-Scale Machine Learning Systems](https://mlsanity.com/paper/2507.01075/) (2025)
- [Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique](https://mlsanity.com/paper/2510.09655/) (2025)
- [In-Memory Indexing and Querying of Provenance in Data Preparation Pipelines](https://mlsanity.com/paper/2511.03480/) (2025)
- [Learning Lineage Constraints for Data Science Operations](https://mlsanity.com/paper/2506.18252/) (2025)
- [TRUST: A Decentralized Framework for Auditing Large Language Model Reasoning](https://mlsanity.com/paper/2510.20188/) (2025)

## Abstract (arXiv)

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.