The Audit Trail Is Not Optional

By Nikki Mehrpoo, JD, AIWC, AIMWC

Founder, The MedLegal Professor™ | AI Legal Governance Strategist | Former Workers’ Compensation Judge

In high-stakes industries—like law, medicine, insurance, and government—outcomes are never enough on their own.

It is not just what your AI produces; it’s how, why, when, and under what conditions. If a system outputs a result and you cannot show your work, it cannot be trusted. In my work, I’ve seen this principle tested time and again, and it holds true now more than ever.

You need a trail. A record. A transparent path from input to output. That is the audit trail, and in the age of AI, it is your most critical tool for maintaining professional credibility and legal defensibility.

What Is an Audit Trail?

An audit trail is the full documentation of what a user or system did, when they did it, how it was done, and what tools or data were involved. Think of it as a behind-the-scenes logbook for everything your AI touches. It records:

• Prompts or instructions used

• Data or inputs provided

• Model or system version

• Output produced

• Human edits or oversight

• Timestamps and user IDs

Audit trails are not about micromanagement. They are about legal defensibility, ethical responsibility, and professional credibility.

Why It Matters in the AI Era

AI makes work faster—but faster work must still be traceable, reproducible, and explainable. In highly regulated fields, no tool should be allowed to generate, suggest, or finalize critical content without documentation of what occurred.

Here is what an audit trail protects:

• You from malpractice and liability claims

• Your clients from undocumented decisions

• Your firm or organization from regulatory scrutiny

• Your system from accusations of bias or fraud

In short, it’s the record that helps you stand up in court, in an audit, or before a licensing board. This brings us back to a fundamental rule that I’ve lived by my entire career, both on and off the bench.

The Professional Standard: “If It’s Not Documented, It Didn’t Happen”

This rule applies in patient charts, legal briefs, claims processing, and now in every AI-enabled workflow. If your system outputs something and you don’t know who prompted it, how it was edited, what source material it referenced, or whether it was reviewed, then it is not usable in a professional setting. Period.

Audit Trails for Novice and Hesitant AI Users

If you are just starting to use AI, here’s the good news: You don’t have to be a tech expert to create a legally sound audit trail. You need a few habits, a few tools, and a mindset that values traceability.

Here’s how to start:

• Use platforms that automatically log prompts, inputs, and outputs.

• Screenshot or save your prompts and results if the platform doesn’t.

• Create a folder system to save source material, drafts, and final versions.

• Always review and annotate outputs with human judgment.

• Label every file with a timestamp and your initials or user ID.

If you’re not doing this, you are operating blind. Always ask yourself: Could I show this document’s history to a court? If asked to explain how this was generated, do I have records? If there’s a mistake, can I track it back to the source? If the answer is no, your AI workflow is a liability.

The Standard for Regulated Industries

In industries governed by legal, clinical, or insurance standards, the audit trail is not a “nice-to-have”—it is mandatory.

No AI system should ever be deployed in these sectors without a plan for:

• Input logging

• Prompt and version capture

• Output storage

• Human-in-the-loop review notes

• Disclosure language

Audit trails make AI responsible. They make compliance possible. They are the bridge between speed and credibility.

FYI: Authority + Access

To help you build your own internal standards, I often point leaders to these globally recognized organizations for guidance on AI auditability, documentation, and compliance:

• NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework

• OECD AI Principles: https://oecd.ai/en/ai-principles

• The Alan Turing Institute: Explainable AI: https://www.turing.ac.uk/research/research-projects/explainable-artificial-intelligence

• World Economic Forum: AI Governance Tools: https://www.weforum.org/agenda/2023/05/ai-governance-framework/

• Google’s Model Cards for Model Transparency: https://modelcards.withgoogle.com/about

My Final Word

AI makes things faster. But licensed professionals are still the ones held responsible. You cannot delegate judgment. You cannot automate accountability. And you cannot prove anything without a documented trail of evidence. The audit trail is not optional.

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