AI Audit & Trust Frameworks
Every framework for AI in the enterprise tells you how to govern the system. None tell a finance leader what to do in the thirty minutes between receiving an AI analysis and acting on it. This is that missing discipline — field-tested on a real 400,000-transaction fleet analysis, built for the people who have to sign their name to the number.
Open access · no sign-up
Finance leaders now work with AI directly. The junior analyst who used to reconcile the numbers before they reached the CFO is gone — and with them went the layer that quietly caught the errors.
Governance standards — NIST, ISO 42001, the Big Four playbooks — all audit AI systems: how models are built, validated, deployed. Excellent work, wrong problem. None govern the moment that decides outcomes: an executive has the analysis in hand and needs to know how much weight the evidence actually supports before acting.
Traditional audit standards are written by regulators and adopted years later. For AI-output audit, that's too slow. This discipline was built from the user side — on a real capital decision — and codified while the ink was still wet. That's what these frameworks are.
Four documents that reference and reinforce each other — a ladder from the real-world proof down to the checklist an analyst runs on a Tuesday. They publish here as each is finalized — free to read and share, no email. Read one, or follow the whole chain.
01 · The proof
The real 400,000-transaction fleet dataset the method was forged on. A three-tier audit that traced every number to source — and found a 142-day gap between the file and the ERP that no formula could have caught.
For executives weighing the evidence behind the method
Publishing soon02 · The operating manual
Fleet decisions fail at three specific moments — Purchase, Performance, Governance — each with its own inputs and failure signature. Its governance gate is where AI-output audit and trust discipline live. Includes self-check tools that score your discipline at each gate.
For dealer principals, GMs & rental leads
Publishing soon03 · The governance layer
The tactical protocol for the pre-action moment: a Trust Equation, the Five Questions before you act, the four executive roles, and a Trust-Tier Assessor that classifies the output on your desk.
For the C-suite & board-facing operators
Read the framework →04 · The practitioner method
The hands-on methodology: six core principles, the three-tier audit model, a phase-by-phase checklist, and reusable prompt templates for audit-ready output. The playbook an analyst works from.
For financial analysts & internal audit
Read the guidebook →The through-line
Each piece answers the next question: whether the method holds in real data, when and who manages the decision, how much to trust the output, and how to audit it in detail.
The Research · Working Paper
The academic foundation beneath the suite. When AI both produces an analysis and gathers the evidence under it in a session no one watched, the ordinary basis for trusting the result disappears — and auditing the model never answers the operative question: is this deliverable actually supported? The paper specifies that missing layer — a substantive, per-deliverable assurance discipline drawn from the audit profession's oldest machinery — arguing that AI erodes the two properties any review depends on, competence and independence, and that independence can't be willed into being; it has to be engineered.
Generative and agentic artificial intelligence now produces the analyses professionals act on and, increasingly, gathers the evidence beneath them without human observation — removing the ordinary basis for trusting a result. The emerging response audits the model: is it fair, documented, governed? That is necessary but does not answer the operative question about a specific output — is this deliverable actually supported? — because the support depends on evidence the model gathered in a session no one watched. This paper specifies the missing layer: a substantive, per-deliverable assurance discipline for AI-assisted knowledge work, drawn from the audit profession's oldest machinery. Its organizing claim is that AI erodes the two properties on which any review depends — the reviewer's competence to detect an error and the reviewer's independence of the answer — and that of these, independence cannot be acquired by resolve and must therefore be engineered. I set out a competence-by-independence frame that prescribes a different remedy in each cell, three supporting components (a Trust-Tier classification calibrating verification to stakes, a net-of-verification measure I call the audit tax, and provenance controls for unobserved agentic evidence), and a reproducible procedure. I demonstrate the discipline in three unrelated bodies of my own AI-assisted work: quantitative business analysis, archival genealogy, and the synthesis of fifteen years of management notebooks. Because a single practitioner designed, performed, instrumented and audited all three, I frame the recurrence across domains as a structured conjecture, not a validated method. A fourth case reports the procedure run prospectively on a live capital-and-personnel decision, with independence supplied by two colleagues drawing on separate systems of record. A blind reconciliation there caught a parsing defect that had silently discarded 37,682 records and understated the headline measure by nearly nine percentage points — a defect that an adversarial re-performance, written from scratch by an independent implementation, had reproduced exactly and therefore missed. One demonstration is supplied as a workbook whose audit chain a reader can re-perform in full.
Keywords: artificial intelligence · knowledge work · verification · provenance · assurance · auditing · research methods · tool-driven discovery
Searcy, DeWayne L., The Auditor's Eye: Manufacturing Independence in AI-Assisted Knowledge Work (July 29, 2026). Available at SSRN.
Three ways in, depending on where you sit. Each points to where the suite begins for you.
If you sign the decision
You get AI analysis and have to act on it. Start with the Trust Framework — run the Trust-Tier Assessor on the output on your desk, then keep the Five Questions as your pre-meeting checklist.
The Trust Framework →If you run the fleet
You own the assets and the purchase calls. Start with the Three Gates — score your discipline at each gate, then read the case study to see all three gates fail, and how the data exposed it.
The Three Gates →If you build the analysis
You produce the numbers others act on. Start with the Audit Guidebook — the checklist, the tolerances, and the prompt templates that make your output audit-ready from the first draft.
The Audit Guidebook →Not a PDF you skim — a tool you use
The frameworks include working assessors, not static checklists. Answer three quick questions about a real decision and the Trust-Tier Assessor tells you how much scrutiny it needs before you act. Try the first one:
If you act on this AI output and it turns out wrong, how hard is it to unwind?
These frameworks didn't start as frameworks. They started as notes from the keyboard — worked out in the open, one post at a time. A rotating handful of them, tagged to the piece they grew into.
On auditing AI output
"Nothing in the output was wrong. That's what made it dangerous."
From LinkedIn · DeWayne Searcy ↗
Why the audit trail matters
"How can I put confidence on the results of an analysis if I am unsure said results are actually derived from the raw data?"
From LinkedIn · DeWayne Searcy ↗
Augmentation vs. outsourcing
"Augmentation compounds. Outsourcing erodes. No productivity gain compensates for the judgment you let atrophy."
From LinkedIn · DeWayne Searcy ↗
Knowing when to close the laptop
"Knowing when not to use AI is part of using it well."
From LinkedIn · DeWayne Searcy ↗
Manage inputs, not outcomes
"You can't manage ROI, net income, or gross profit. They're lagging indicators — the scoreboard at the end of the game, not the plays that won it."
From LinkedIn · DeWayne Searcy ↗
Data-driven, done right
"Quantify first, then qualify. Let the data do the talking, first."
A working maxim · DeWayne Searcy
This is a public resource, built in the open. Nothing here sits behind an email wall or a paywall — the point is the thinking, and the discipline behind it.