This question is on the desk of every IT and security lead right now. A ban is a policy decision; an alternative is an architecture decision, and the two cannot be settled in the same meeting. Below, the decision is reduced to three criteria.
Criterion 1 — Visibility
The first question: can you see the usage? A ban, by definition, reduces visibility to zero. Usage continues, moves to personal accounts and phones, and the organisation no longer knows which team is sending which data where.
With a tool the organisation provides, every request is a log line: who, when, with which document, in which data class. That log is both the strongest evidence the organisation holds in an audit under KVKK, Turkey's personal data protection law, and the only measure of whether the tool is actually useful.
The decision question: in six months, will you be able to answer "who is using AI, and for what?"
Criterion 2 — The data boundary
The second question: where does the data physically go? There are three options, each with a different responsibility profile.
- A cloud service abroad. The fastest start; but where personal data is involved, every request is a cross-border transfer and establishing its legal basis is your job.
- The organisation's own server (on-premises). Data never leaves the boundary; the model works without an internet connection. There is a one-off hardware cost and no monthly per-user fee.
- Hybrid. Public or non-sensitive work in the cloud; anything involving customer or employee data on-premises. For most organisations this is the balanced point.
A simple rule: the higher the data class, the closer the model should sit to the organisation. Cloud is fine for public marketing copy; customer records, contracts and personnel files need a model that runs under the organisation's control. In regulated fields such as finance and health this is not a preference but a requirement.
Criterion 3 — Cost and friction
The third question: what is the real cost? Cloud services charge per user per month, and the bill grows as adoption spreads. On-premises, the cost comes up front, but one fact is often underestimated: today a single powerful workstation can run a model that covers the daily needs of a team of five to ten. A pilot does not need a data centre.
A ban has a cost too; it is forgotten because it never appears on an invoice. The employee who could finish a task in thirty minutes with AI finishes it in three hours, or finishes it in thirty minutes somewhere the organisation cannot see. Either way, the organisation loses.
A ban looks cheap because its price is paid not on an invoice but in invisible usage and lost hours.
Decision summary
- Public or non-sensitive data → a cloud service is acceptable, still under a corporate account and contract.
- Personal data, customer records, contracts → on-premises or hybrid; data must stay inside the boundary.
- Regulated data (finance, health, public sector) → on-premises; logging and access control are mandatory.
The policy to write instead of a ban
A good AI policy fits on one page and says five things:
- Which tool is approved and where it is accessed.
- Which data classes may be entered, and which may not.
- Who is responsible for the output (the person using it; the tool is an assistant, not a signatory).
- How usage is monitored and how long logs are kept.
- How often the policy is reviewed.
To write those five items there has to be a tool to point to. A ban is a sentence; an enforceable policy requires an architecture.
Where your organisation stands on these three criteria today, and which setup each data class requires, is something we settle together in a discovery call.