Timeline
10-18 working days
Shadow AI Audit
Map the AI your company does not officially control.
A shadow AI and leak audit for organizations where teams use unmanaged prompts, extensions, agents, automations, or copilots with company data.
The Shadow AI Audit defines a leak as data, cost, quality, ownership, or vendor control leaving the operating frame without being visible to leadership.
Details
Indicative price
€7k-€20k depending on sensitivity and departments covered
definition
Definition
The Shadow AI Audit defines a leak as data, cost, quality, ownership, or vendor control leaving the operating frame without being visible to leadership.
who
Who it is for
This page is for leaders, finance, operations, and IT teams that already have visible AI usage but not enough evidence to decide.
framework
Framework
We use the Four Leaks of AI ROI: spend, adoption, leaks, and role drift. Every signal is tied to a cost, an owner, and a decision.
deliverables
Deliverables
- Shadow AI inventory by team and data class
- Leak risk map for data, cost, vendor, and output quality
- Policy gaps and safe usage paths
- Priority controls for sensitive workflows
- Executive action plan with quick wins and governance backlog
sample-report
Sample report
Heatmap of exposed data, unmanaged tools, and unowned workflows.
Control backlog with owner, effort, impact, and urgency.
timeline
Timeline
10-18 working days
price-band
Price band
€7k-€20k depending on sensitivity and departments covered
objections
Common objections
- The audit does not shame teams for using AI; it turns hidden work into manageable work.
- It starts with practical exposure, not legal panic.
- It separates low-risk experimentation from sensitive workflows that need controls.
Deliverables
- Shadow AI inventory by team and data class
- Leak risk map for data, cost, vendor, and output quality
- Policy gaps and safe usage paths
- Priority controls for sensitive workflows
- Executive action plan with quick wins and governance backlog
Objections
- The audit does not shame teams for using AI; it turns hidden work into manageable work.
- It starts with practical exposure, not legal panic.
- It separates low-risk experimentation from sensitive workflows that need controls.
FAQ
Do we need perfect data first?
No. The audit separates available evidence, reasonable assumptions, and the measurements to install next.
Is this a technical project?
Not first. The starting point is the business decision: cost, value, risk, owner, and stop threshold.
Will teams have to stop using their tools?
Only when a use case proves nothing or exposes too much. Useful tools are protected and measured better.
What happens after the audit?
You leave with a decision: stop, consolidate, fix, scale, or govern each priority use case.
Shadow AI Audit