[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85670-en":3,"doc-seo-85670-105":28,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},85670,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows","AI systems increasingly support consequential, regulated decisions in domains such as auditing, finance, and healthcare, creating a key requirement: organizations must reconstruct which evidence informed a specific conclusion and prove the reasoning record was not altered. AuditWeave is a lightweight, dependency-free Python library that records AI-assisted and data-transformation workflow steps into an append-only, hash-chained ledger. A shared event vocabulary spans RAG pipelines and tabular/lakehouse transformations, enabling end-to-end traceability. Sealed-ledger verification detects any event modifications, reorderings, insertions, or deletions, and evaluation reports microsecond-level overhead with correct tamper detection across randomized trials.","arXiv :2607 .09682v1 [ cs .LG] 14 Jun 2026  \nAuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation  \nWorkflows  \nVimal Nakrani  \nIndependent Researcher  \n[https://pypi.org/project/auditweave/](https://pypi.org/project/auditweave/)  \nAbstract  \nAI 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 toreconstruct, 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  \n2,000 randomized trials.  \n1 Introduction  \nArtificial-intelligence systems are moving from experimental tools to components embedded in consequential, regulated workflows. A growing share of this usage is assistive: a model reads a body of source material and proposes a conclusion that a human then relies upon. Auditors use retrieval-augmented systems to navigate enormous transaction datasets; financial institutions apply AI to credit and fraud workflows; healthcare organizations use it to support prior-authorization decisions. In each case, the value of the system is inseparable from a question that regulators are increasingly asking: when an AI-influenced decision is made, what evidence informed it, and can the organization prove that the record of that reasoning is intact?  \nThis question has two distinct parts. The first is provenance: the ability to reconstruct, for any conclusion, the chain of source documents, retrievals, transformations, and model outputs that produced it. The second is integrity: the ability to demonstrate that the recorded chain has not been altered—whether by accident or intent—after it was created. Both are prerequisites for the kind of accountability that regulated environments demand, and neither is fully addressed by current tooling.  \nExisting tools address related but distinct problems. Model-observability and monitoring platforms focus on operational concerns—latency, cost, drift, and performance—and are designed around the needs of the machine-learning engineer operating a system in production. Governance and model-risk platforms produce compliance reports and inventories. These are valuable, but they answer a different question than the reviewer’s: given one conclusion, show me the evidence beneath it, in order, and assure me the record was not changed. Source attribution—binding an AI output to the specific inputs that produced it—has been identified as a particularly weak link in current AI-audit implementations.  \nThis paper presents AuditWeave, a small library, with no runtime dependencies, that addresses both parts directly. AuditWeave records workflow steps into an append-only, hash-chained ledger using a compact, system-agnostic event vocabulary. ","cbCairL8bsnL0M3n","https://ap.wps.com/l/cbCairL8bsnL0M3n","pdf",259713,1,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Background and Related Work","[{\"question\":\"What problem does AuditWeave address in AI-assisted decision workflows?\",\"answer\":\"It enables reconstruction of the evidence behind a specific AI-influenced conclusion and provides assurance that the recorded reasoning chain was not modified after creation.\"},{\"question\":\"How does AuditWeave store workflow information for auditability?\",\"answer\":\"It records steps into a single append-only, hash-chained ledger using a compact, system-agnostic event vocabulary.\"},{\"question\":\"What types of workflow components can be traced end-to-end with AuditWeave?\",\"answer\":\"A unified event vocabulary covers retrieval-augmented generation (RAG) pipelines and tabular or lakehouse data-transformation workflows, allowing one trace across mixed workflows.\"}]",1784205502,20,{"code":4,"msg":29,"data":30},"ok",{"site_id":23,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":26},"auditweave-a-tamper-evident-auditor-navigable-evidence-layer-for-ai-assisted-and-data-transformation-workflows","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/auditweave-a-tamper-evident-auditor-navigable-evidence-layer-for-ai-assisted-and-data-transformation-workflows/85670/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What problem does AuditWeave address in AI-assisted decision workflows?","Question",{"text":73,"@type":74},"It enables reconstruction of the evidence behind a specific AI-influenced conclusion and provides assurance that the recorded reasoning chain was not modified after creation.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does AuditWeave store workflow information for auditability?",{"text":78,"@type":74},"It records steps into a single append-only, hash-chained ledger using a compact, system-agnostic event vocabulary.",{"name":80,"@type":71,"acceptedAnswer":81},"What types of workflow components can be traced end-to-end with AuditWeave?",{"text":82,"@type":74},"A unified event vocabulary covers retrieval-augmented generation (RAG) pipelines and tabular or lakehouse data-transformation workflows, allowing one trace across mixed workflows.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,125,128,132],{"id":20,"doc_module":4,"doc_module_name":44,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":45,"doc_module":4,"doc_module_name":44,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":44,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":44,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":44,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":44,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":44,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":44,"category_name":123,"show_sort_weight":27,"slug":124},9,"Religion & Spirituality","religion-spirituality",{"id":27,"doc_module":4,"doc_module_name":44,"category_name":126,"show_sort_weight":27,"slug":127},"World Cup","world-cup",{"id":129,"doc_module":4,"doc_module_name":44,"category_name":130,"show_sort_weight":129,"slug":131},10,"Lifestyle","lifestyle",{"id":133,"doc_module":4,"doc_module_name":44,"category_name":134,"show_sort_weight":104,"slug":135},19,"General","general"]