The operating result.
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 paths, 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
- AI Transformation workstream unlocked
- AI Governance workstream unlocked
- AI Engineering track scoped
Turn policy into proof.
Unified policies are not the same as unified AI. 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.
- the golden-dataset substrate that supports the evidence layer
Proof.
- 75 shadow-AI cases across a scaled workforce audit scope.
- 20 unauthorized MCP paths 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: not "we have a policy" but "here is every tool, every owner, every path, under one cadence."
Next move: AI Audit (2 weeks) AI Governance (Continuous) AI Engineering (production hardening).
Start with a quick audit.
Two weeks to the operating read: AI value, AI risk, owners, the shadow AI and MCP paths you cannot currently see, and the next funded workstream.