AI governance in the enterpriseAI is already in your company. It’s time to organise it.
AI governance is not about banning. It is about deciding who can do what, with which models, on which data and at what cost, and then building on what works.
Governing AI in a company means framing uses that already exist: defining approved providers and models, protecting data before it is sent to a model, measuring and capping usage, and turning useful tools from individual workarounds into maintained, approved and shared applications.
AI in the enterpriseThe situation: usage moving faster than the rules
In most SMEs and mid-sized companies, AI did not arrive through a project. It arrived through employees: a personal ChatGPT subscription, a trial of Claude, Copilot switched on in the office suite, Gemini on a phone. Uses have multiplied without a framework, and often without management or the IT department having a clear picture of them.
This is not necessarily a discipline problem. It is a sign that teams have found real value. But that value remains scattered, and so do the risks.
Risks: data, costs, know-howWhat the lack of governance costs
- Data leaking out: customer documents, contracts, personal data pasted into a consumer tool;
- hidden costs: individual subscriptions, untracked API usage;
- non-reproducible results: an effective prompt stays in one person’s history;
- no knowledge retention: each team reinvents what another has already found;
- dependence on individuals: the tool disappears with the person who built it.
Banning AI, ChatGPT Enterprise, AI policyCommon approaches and their limits
Banning
An outright ban pushes usage towards personal tools, out of sight. The risk increases instead of decreasing.
Rolling out a single assistant
Giving everyone a company assistant is a useful step: data stays within a contractual framework. But a conversational assistant does not turn individual use into a process. Everyone keeps redoing their own manipulations on their own.
Writing a policy
An acceptable use policy sets out principles. It does not measure usage, control the models being used or capture any tool.
What AI governance must coverWhat operational governance must cover
- Approved models
- Protected data
- Approved applications
- Measured usage
- Caps and alerts
- Captured know-how
Governance becomes concrete when it applies where AI is used: in the applications themselves, not in a separate document.
Analysing a sensitive document with AIAn example: analysing a sensitive document
A lawyer or a buyer wants an AI to analyse a contract. Without a framework, they copy the text into the tool of their choice, personal data included. With a governed application, the document first goes through an anonymisation step, then a check, before the model approved by the company is called. The result is returned to the user; the usage is counted against that application. Data detection and masking are configurable; for sensitive documents, human review may still be needed.
- Document
- Anonymisation
- Check
- AI
- Result
Operational AI governanceWhat Collapps brings
Hi-perf Collapps is designed to make governance operational:
- The IT department defines the approved providers and models.
- The company uses its own AI accounts and contracts.
- Usage is measured per application, with quotas, caps and alerts.
- Useful tools become applications versioned in Git, tested and then approved before production.
- They are published in an internal portal, with per-application permissions.
See how Collapps works in detail.
AI policy, DPO, IT departmentPoints to watch
No tool replaces the company’s own decisions: which data may be processed by which model, who can build applications, who approves. These choices are prepared with the IT department and, where relevant, with the DPO. The platform then applies them systematically.
AI governanceFrequently asked questions
Do we have to choose a single AI provider?
No. The company can approve several providers and models, depending on the use. What matters is that the choice is made and enforced, rather than left to each employee.
How can we track what AI costs?
By measuring usage where it happens: per application. That is what makes it possible to set relevant quotas and caps, and to compare cost with the value produced.
Where should we start?
With an honest inventory of existing uses and the most costly business pain points. The first governed applications often grow out of tools that teams have already cobbled together.
Your teams already use AI. Let’s organise it.
Let’s talk about your current uses, your constraints and the first applications to govern.