AuditWeave Creates Tamper-Evident Audit Trails for AI Workflows

Organizations operating in regulated sectors like auditing, finance, and healthcare face a critical need to reconstruct the evidence that informed consequential AI-assisted decisions and to prove the integrity of those records. Existing tools often focus on model observability or governance reporting, catering more to machine learning engineers than to auditors needing to trace specific conclusions. To address this gap, AuditWeave has been introduced. AuditWeave is a lightweight Python library designed to record the steps of AI-assisted and data-transformation workflows into a single, append-only, hash-chained ledger. It uses a small, system-agnostic event vocabulary that can span both Retrieval-Augmented Generation (RAG) pipelines and tabular data transformations, allowing for end-to-end traceability of conclusions that draw on diverse data sources. The hash-chain construction ensures that any modification, reordering, insertion, or deletion of events within a sealed ledger is immediately detectable through verification. The tool boasts minimal overhead, costing tens of microseconds per event, and has demonstrated 100% tamper-detection correctness in randomized trials.