AuditWeave lets auditors trace AI decisions with tamper-proof logs

As AI systems increasingly influence decisions in regulated domains like auditing, finance, and healthcare, organizations face a recurring obligation: after the fact, they must reconstruct which evidence informed a given conclusion and prove the record hasn't been altered. Existing tools focus on model observability or drift monitoring, but they're built for ML engineers, not auditors needing to trace a specific conclusion back to its supporting evidence.

Vimal Nakrani's AuditWeave solves this with a lightweight Python library (no runtime dependencies) that records each step into a single append-only, hash-chained ledger. A system-agnostic event vocabulary spans both retrieval-augmented generation (RAG) pipelines and tabular/lakehouse transformations, so hybrid conclusions remain traceable end-to-end. Performance testing shows integrity guarantees cost tens of microseconds per event. In 2,000 randomized trials, chain verification flagged every injected mutation across four classes (modification, reordering, insertion, deletion). The open-source implementation is available on GitHub. AuditWeave provides the tamper-evident evidence layer that regulated industries need for trustworthy AI-assisted workflows.