A 350-employee financial services firm generating $82M in annual revenue. Regulated industry. Client fiduciary obligations. The compliance team had done the right things: written an AI acceptable use policy, maintained an approved tool list, scheduled quarterly reviews. On paper, they were governed. Then the external auditor arrived for their annual review and asked a question nobody expected: "Can you prove any of this is actually enforced?"
They couldn't. The policy existed in a SharePoint folder. The approved list was a spreadsheet last updated 4 months ago. The quarterly reviews had happened twice, then stopped. And in the gap between policy and reality, 23 unapproved AI tools had entered the environment — 7 of them handling sensitive client financial data.
The compliance team had a policy. What they didn't have was evidence. No automated detection of new AI tools. No continuous monitoring of data flows. No cryptographic proof that evaluations had been performed. No audit trail an external examiner could verify independently. The governance framework existed — but it existed as a document, not as a system. And in a regulated industry, that distinction matters enormously.
Most governance tools generate logs. Logs can be edited. Logs can be backdated. Logs require trust in the system that produced them. In a regulated environment where an external auditor needs to independently verify compliance, logs aren't enough. We deployed cryptographic governance — every evaluation signed, every chain verified, every proof externally auditable.
Each finding scored on a 5-point weighted model: compliance risk, data exposure, detection speed, governance maturity, and regulatory impact.
| Finding | Score | State at Audit | State After |
|---|---|---|---|
|
7 Unapproved Tools on Client Financial Data
Compliance · Data Governance
|
5.00 Do First | 7 tools processing client portfolio data, account numbers, and transaction histories — no DPA, no evaluation, no approval | All 7 tools remediated — 4 migrated to approved alternatives, 3 had data flows restructured with DPAs executed |
|
23 Unapproved Tools — 74% of AI Stack Ungoverned
Governance · Visibility Gap
|
4.80 Do First | Approved list had 8 tools — actual environment had 31 — governance covered only 26% of the real AI footprint | 94% governance coverage — 29 of 31 tools evaluated, 2 in active evaluation pipeline |
|
No Automated Detection — 90-Day Blind Spot
Detection · Monitoring Gap
|
4.50 Do First | New AI tools could operate for ~90 days before manual review cycle discovered them — if the review happened at all | Mean time to detect reduced to ~4 hours via Proof Sensor and network-level AI domain monitoring |
|
Quarterly Review Cycle Abandoned
Governance · Process Discipline
|
3.70 Do Next | Quarterly reviews happened twice and then stopped — no accountability mechanism, no automated reminders, no escalation path | Automated review triggers — tools flag for re-evaluation at 90 days, escalation if review overdue, compliance dashboard for tracking |
|
No Verifiable Audit Trail for Tool Approvals
Compliance · Evidence Gap
|
4.60 Do First | Tools "approved" by adding to a spreadsheet — no evaluation record, no reviewer identity, no tamper detection, no external verifiability | 847 Ed25519-signed evaluations in a SHA-256 hash chain with external trust anchor — auditor-verifiable without trusting internal systems |
Governance coverage measures the percentage of AI tools in the environment that have been formally evaluated, categorized, and either approved with controls or blocked. 94% means 29 of 31 tools are governed — 2 are in active evaluation with interim controls in place.
The external audit finding was raised and cleared in the same session. The auditor verified the hash chain independently, confirmed evaluation integrity, and documented that the governance framework met the firm's regulatory obligations. The compliance officer now presents AI governance status to the board quarterly — with cryptographic proof that the numbers are real.
The audit finding is cleared. The framework is live. Phase 2 extends cryptographic governance to new AI tool categories and builds regulatory reporting automation.
We build AI governance frameworks that don't just exist on paper — they produce cryptographically verifiable evidence that survives external examination.
Start Your Governance Audit →Disclaimer: This use case is based on a simulated engagement using the Coriven Method. Company details are representative. All findings reflect the methodology Coriven applies to real engagements. Green numbers are verified from source data. Indigo numbers are calculated with documented methodology. Gold numbers are estimated from baseline data. Actual results vary.
Every number in this use case is confidence-tagged by color — because we believe if we can't prove it, we should say so.