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This is an earlier engagement or reference design, preserved as authored. Reported metrics and delivery status have not been independently revalidated for this preview; source dates are not established here. Modeled figures and design targets are not realized results. This record does not substantiate outcomes for the proposed product.
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Prove the AI You Could Not See.
75 shadow AI cases found, 20 unauthorized MCP paths exposed, 12 sanctioned tools moved into cadence.
The exposure for a PE-backed leadership team is the gap between “we have a policy” and “we can prove control” when the sponsor, the board, or a buyer asks. You cannot set a strategy for AI you cannot see. The AI Audit gave them the first unified count, then the evidence to fund the right workstreams and contain the right risks.
Start with the operating shape.
A PE-backed regulated-finance platform, rolled up from seven entities, where AI usage was already ahead of policy, procurement, and control coverage.
Map value and risk.
The AI Audit turned fragmented signals into a board-readable findings layer.
Found across a scaled audit scope, roughly 4x the count in the pre-audit IT register, and tied to owner, tool, and data sensitivity.
MCP-shaped grants and endpoint findings sat outside DLP and CASB coverage before moving into the remediation queue.
Approved tools moved into the operating cadence so value capture and risk containment could be reviewed together.
Engineering carried the largest data-leakage risk. GTM was second, because customer and prospect data moved through unsanctioned tools.
75 shadow AI cases across a scaled workforce audit scope, roughly 4x the count in the pre-audit IT register.
20 unauthorized MCP connections, every one outside existing DLP and CASB coverage.
Engineering carried the largest data-leakage exposure. GTM was second.
Move findings into cadence.
The important shift was from finding-state to outcome-state.
Ship the board read.
The two-week deliverable is the entry artifact. Deeper engineering and governance workstreams unlock after the read.
Endpoint discovery
Identity correlation
OAuth grant inventory
MCP scan
Risk classification
Consolidation roadmap
Framework mapping
Operator findings readout
Board-ready operating read
Build track scoped
Run scoped for the live agents
Board read of every tool and path
Turn policy into proof.
A unified policy still left the AI itself unmapped. The audit shipped the operating read first, then named the workstreams with evidence behind each next move, so the team could turn “we have a policy” into “we can prove control” in front of the board.
What Shipped.
Shadow-AI inventory and baseline DLP view across priority workflows.
Shadow MCP Discovery extension for AI paths DLP and CASB miss.
A consolidation roadmap that separated tool overlap from genuine workflow need.
Quarterly board operating cadence, with value capture, risk containment, and evidence mapped to ISO 42001 and NIST AI RMF.
AI Engineering side-track scope for AWS-hosted production AI, developer AI tooling guardrails, and CI/CD AI hardening.
Proof.
75 shadow AI cases across a scaled workforce audit scope.
20 unauthorized MCP connections outside the existing control plane.
12 sanctioned tools moved into the operating cadence.
4-tool consolidation roadmap.
Evidence pipeline mapped to ISO 42001 and NIST AI RMF requirements.
What the board can now defend.
This pattern applies to PE-backed regulated-finance and fintech platforms where a roll-up moved faster than the AI inventory. The audit gives leadership the first defensible answer to the sponsor, the board, and a future buyer: “here is every tool, every owner, every path, under one cadence.”
Next move: review one live agent for reliability, then decide whether Run or Build is needed.
Start with a reliability read.
Two weeks to the operating read: AI value, AI risk, owners, the shadow AI and MCP paths you cannot currently see, and what to fix first.
Related links and sources
Related links and sources
The challenge
A PE-backed regulated-finance platform rolled up from seven merged entities. Leadership had done the visible integration and believed AI was handled. It was not. AI usage was already ahead of policy, procurement, and control coverage, and the board had no single count of what was running, who owned it, or where data was going.
The approach
Shadow-AI inventory and baseline DLP view across priority workflows.
Shadow MCP Discovery extension for AI paths DLP and CASB miss.
A consolidation roadmap that separated tool overlap from genuine workflow need.
Quarterly board operating cadence, with value capture, risk containment, and evidence mapped to ISO 42001 and NIST AI RMF.
AI Engineering side-track scope for AWS-hosted production AI, developer AI tooling guardrails, and CI/CD AI hardening.
Artifacts and handoff
75 shadow AI cases across a scaled workforce audit scope, roughly 4x the count in the pre-audit IT register.
20 unauthorized MCP connections, every one outside existing DLP and CASB coverage.
Engineering carried the largest data-leakage exposure. GTM was second.
Reported measures and design targets
75 shadow AI cases across a scaled workforce audit scope.
20 unauthorized MCP connections outside the existing control plane.
12 sanctioned tools moved into the operating cadence.
4-tool consolidation roadmap.
Evidence pipeline mapped to ISO 42001 and NIST AI RMF requirements.
Explore the failure mechanisms, the tests they need and the evidence that establishes the customer outcome.
AI reliability, from production to customer outcome.