# Audit Trails for Accountability in Large Language Models

arXiv: https://arxiv.org/abs/2601.20727 · HTML: https://mlsanity.com/paper/2601.20727/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2026.

## Built on by (most cited first, 12 of 12)

- [From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents](https://mlsanity.com/paper/2606.04990/) (2026)
- [Auditable Agents](https://mlsanity.com/paper/2604.05485/) (2026)
- [Reinforcement Learning from Human Feedback: A Statistical Perspective](https://mlsanity.com/paper/2604.02507/) (2026)
- [Responsible Agentic AI Requires Explicit Provenance](https://mlsanity.com/paper/2605.17169/) (2026)
- [Agent Flight Recorder: Tamper-Evident Audit Trails with On-Chain Anchoring for Long-Horizon Tool-Using Agents](https://mlsanity.com/paper/2609.01931/) (2026)
- [Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On](https://mlsanity.com/paper/2605.19035/) (2026)
- [Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response](https://mlsanity.com/paper/2607.25379/) (2026)
- [Security and Privacy in Agentic AI: Grand Challenges and Future Directions](https://mlsanity.com/paper/2607.06608/) (2026)
- [AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows](https://mlsanity.com/paper/2607.09682/) (2026)
- [Schema-Bound LLM Control of Scientific Instrumentation through Model Context Protocol Skills](https://mlsanity.com/paper/2607.17012/) (2026)
- [Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness](https://mlsanity.com/paper/2607.20729/) (2026)
- [Pollard: Governed Semantic Execution Trees for Framework-Independent AI Agent Control and Replay](https://mlsanity.com/paper/2610.16145/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly embedded in consequential decisions across healthcare, finance, employment, and public services. Yet accountability remains fragile because process transparency is rarely recorded in a durable and reviewable form. We propose LLM audit trails as a sociotechnical mechanism for continuous accountability. An audit trail is a chronological, tamper-evident, context-rich ledger of lifecycle events and decisions that links technical provenance (models, data, training and evaluation runs, deployments, monitoring) with governance records (approvals, waivers, and attestations), so organizations can reconstruct what changed, when, and who authorized it. This paper contributes: (1) a lifecycle framework that specifies event types, required metadata, and governance rationales; (2) a reference architecture with lightweight emitters, append only audit stores, and an auditor interface supporting cross organizational traceability; and (3) a reusable, open-source Python implementation that instantiates this audit layer in LLM workflows with minimal integration effort. We conclude by discussing limitations and directions for adoption.