For CIOs, CTOs and leadership
Shadow AI: a review of what is already built Shadow AI: what your people have built with AI and nobody has reviewed
Shadow AI is everything the people in a company use or build with artificial intelligence without the IT department knowing about it or having reviewed it. It includes the AI tools each person uses on their own and also the reports, dashboards, automations and programs made with them that are already running.
Updated on
Security tools detect and block AI use. We review what your people have already built with it and is now running: how it calculates, what data it touches and whether it holds up.
What shows up when nobody reviews
Miscalculated KPIs
A formula that counts twice or a source that stopped updating. The dashboard looks fine and leadership decides on a wrong number.
Exposed data
Reports shared through an open link, passwords inside the code, or personal data copied into tools nobody has inventoried.
Code that doesn't scale
What worked with ten users and a month of data falls over with the whole company and three years of history.
No owner
Whoever built it changed jobs or left the company. Nobody knows what it does, what it depends on or what breaks if it is touched.
What we review in what is already running
- The calculation of each KPI, checked against its source
- What data each report uses and who can see it
- Passwords, access and public links
- Performance with the real volume of data and users
- What depends on what, and who maintains it
We don't detect or block AI use: there are tools for that. We review what is already built, and your team decides what stays, what changes and what is retired.
The reviews that cover it
Each one has its own page: what we review, why it matters and what you get.
Power BI health check
Reports built on stale or broken data, refreshes that fail and licenses assigned to people who no longer use them.
On request- Refreshes that fail or fall behind
- Reports that read broken sources
- Licenses with no activity
Microsoft Fabric health check
Pipelines that feed reports with stale or broken data, and capacity that is paid for but not used.
On request- Pipelines that fail or load stale data
- Capacity usage and cost
- Workspace access and inactive users
Database review
PostgreSQL, Redis, Elasticsearch and RabbitMQ, in read-only mode: structure, indexes, slow queries, locks and connections.
More than 200 control points- Tables, indexes and migrations
- Slow queries, locks and connections
- Redis memory, Elasticsearch disk and RabbitMQ queues
Backend and API review
The server side behind your app or your website: what the API exposes, what it protects, what headers it serves and what ends up in its logs.
More than 150 control points- Exposed fields and unprotected routes (OWASP API3 and API5)
- Route inventory and endpoint contract
- Security headers and personal data in logs
What you get, every time
One report for two readers: one page for leadership and the detail with evidence for your team.
The health status
For each system reviewed: code, apps, website, backend, databases, server and platforms.
Every finding checked
With the evidence of where it is and why it matters.
The list ranked by impact
What to fix first, what can wait and what doesn't matter.
A meeting and a page
We walk your team through it, and leadership gets a one-page summary.
Frequently asked questions
What is shadow AI?
It is what the people in a company use or build with artificial intelligence without IT knowing about it or having reviewed it: reports, dashboards, automations and programs that are already running.
What is shadow IT, and how is it different from shadow AI?
Shadow IT is the software used in a company without IT knowing. With shadow AI, people also build new things with the tools they use: reports, calculations and programs that decisions are later based on.
How do you detect shadow AI, and do you have to block AI tools?
You detect it with an inventory: area by area, what teams use and what they have built with AI, and what data each thing touches. Blocking is a decision for you and your security tools; the review blocks nothing, it looks at what is already running.
My employees are building things with AI without IT knowing. How do I get it under control, and who can help?
Start by finding out what exists. Security tools tell you which AI applications are in use and let you block them. Sondeon reviews what is already built and running: we build the inventory with you and tell you, for each piece, how it calculates, what data it touches and who maintains it.
Do we have to stop what is already working, and who will maintain it?
You don't have to stop anything: the review looks and measures. For each piece it tells you how it calculates, what data it touches and who maintains it, or whether nobody does. Your team decides what stays, what changes and what is retired, with the list and the evidence.
Why is shadow AI a risk, and is a one-time audit enough?
Because people make decisions and touch data with what they build, and IT has not reviewed it. A one-time review falls short: you need a health check, an expert review of what is already built, repeated every three months, because something new appears every week.
Request your free meeting
Fill in the form and we email you to set a day and a time. It is 30 minutes to see what you have and what to review first.