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 · publishing 2026–2028
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.
Five documents that reference and reinforce each other — a ladder from the intellectual foundation 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 foundation
A DuPont lens on why ROI, margin, and net income can't be managed — only produced. The work of finance is to own the upstream behaviors that create them.
For finance & management accountants
Publishing soon02 · The proof
The framework tested against real data across 39 months of a rental fleet. A three-tier audit that found a 142-day gap between the file and the ERP the formulas could never have caught.
For executives & consulting audiences
Publishing soon03 · The operating manual
Fleet decisions fail at three specific moments — Purchase, Performance, Governance — each with its own inputs and failure signature. Includes self-check tools that score your discipline at each gate.
For dealer principals, GMs & rental leads
Publishing soon04 · 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
Publishing soon05 · 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
Publishing soonThe through-line
Each piece answers the next question: why inputs matter, whether it holds in real data, when and who manages it, how much to trust it, and how to audit it in detail.
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."
From LinkedIn · 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.