2-week AI Audit
AI AuditVisibility, risk, outcomes.AI TransformationCapture AI value across workflows.AI GovernanceContinuous evidence, not paperware.AI FluencyWorkforce that compounds.AI engineeringBuild AI inside your product.EvalsContinuous evaluation in production.All servicesThe full service map.
Not sure where to start?Take the 5-min maturity assessment →Maturity modelOpen assessment
By industry
Private equityPortfolio-wide value capture.Regulated financeThe finance umbrella.Banks & capital marketsRegulator-defensible under SR 11-7.FintechShip trusted AI fast.Asset & wealthFor advisors and ops.REITs / real estateAI-driven NOI uplift.InsuranceUnderwriting + claims, instrumented.
Guides, templates, frameworks
Field notesEditorial reads on where AI work stalls.GuidesFinance AI systems, controls, and evidence.FrameworksLevers, maturity models, and control maps.TemplatesAudit memos and reusable eval artifacts.ScorecardsFast reads on adoption, strategy, and governance.Case studiesProof from AI work in finance.Golden datasetsReusable test sets for AI evaluation.NL-to-SQL evalsAudit-grade evidence for finance data agents.AI Audit resourcesInventory, Shadow AI, and board evidence.All resourcesBrowse the complete editorial library.
Featured field note
Why Bank AI Budgets Get Approved and Don't Ship.
Why approved AI spend still fails to produce owned outcomes.
Read more →
AI Audit · Financial services + PE portfolio companies

From shadow AI to board-ready control .

For financial-services companies and PE portfolio companies.

75 shadow AI cases found, 20 unauthorized MCP paths exposed, 12 sanctioned tools moved into cadence.

The audit gave operators and sponsors the evidence to fund the right workstreams and contain the right risks.

Start with the operating shape.

Financial-services companies and PE portfolio companies where AI usage is already ahead of policy, procurement, and control coverage.

WorkstreamAI Audit + AI Engineering + AI Governance
ScenarioFinancial services / PE portfolio company pattern
Next moveAI Audit (2 weeks) → AI Transformation (4-6 months) → AI Governance (Continuous).

Map value and risk.

The AI Audit turned fragmented signals into a board-readable findings layer.

75
Shadow AI cases

Found across a scaled audit scope, roughly 4x the count in the pre-audit IT register, and tied to owner, tool, and data sensitivity.

20
Unauthorized MCP connections

MCP-shaped grants and endpoint findings sat outside DLP and CASB coverage before moving into the remediation queue.

12
Sanctioned tools

Approved tools moved into the operating cadence so value capture and risk containment could be reviewed together.

#1 / #2
Engineering, then GTM

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.

Before the AI Audit
After the AI Audit, Week 2
Near-zero visibility on AI usage outside the IT register.
75 tools mapped and classified by data sensitivity.
20 MCP connections running outside DLP and CASB coverage.
Every connection entered a remediation queue sorted by exposure.
No board-readable AI value and risk view.
Quarterly operating cadence moved onto the board agenda.
Distributed teams, heterogeneous tooling, no shared inventory.
One audit substrate spanning priority workflows.
ISO 42001 alignment was a vague intent.
Evidence pipeline mapped to ISO 42001 and NIST AI RMF requirements.

Ship the board read.

The two-week deliverable is the entry artifact. Deeper engineering and governance workstreams unlock after the read.

Week 1
  • Endpoint discovery
  • Identity correlation
  • OAuth grant inventory
  • MCP scan
Week 2
  • Risk classification
  • Consolidation roadmap
  • Framework mapping
  • Operator findings readout
Day 14
  • Board-ready operating read
  • AI Transformation workstream unlocked
  • AI Governance workstream unlocked
  • AI Engineering track scoped

Name the workstreams that compound.

The audit shipped the operating read first, then named the workstreams with evidence behind each next move.

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 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.

Build inside finance SaaS.

This engagement pattern applies to PE-backed financial-services and fintech software companies. The same evidence pipeline scales to AI shipping inside customer environments.

See AI Engineering →

Start with the 2-week AI Audit.

Leave with the operating read: AI value, AI risk, fluency gaps, owners, and the next funded workstream.