An SME that puts its teams on a cloud AI runs exactly the same risks as a large group, with none of the means to absorb them. No legal department to comb through the terms of service, no security team to watch the data flows, no budget to take the hit of a leak or an emergency compliance overhaul. And it is often the SME that has the most to lose: a customer file, a GDPR breach, or the disappearance of a tool the whole organization depends on. The three main risks can be identified, and each one can be neutralized.
When a large group adopts a cloud AI, it negotiates a custom contract, has the data flows audited by its security team, and provisions for the residual risk. An SME clicks "accept the terms" and puts its teams to work. The risk is the same, the capacity to absorb it is not. This asymmetry is what makes the subject serious for a small structure, where it remains manageable for a large one.
Under the terms of several consumer platforms, what your teams type in can be reused to improve the model. A quote, a customer file, or a business strategy pasted into an assistant can thus feed a system you don't control, and which your competitors use too.
What neutralizes it: an assistant deployed on your premises, or at minimum an offering that contractually rules out training on your data and where you keep control of your own keys.
A service operated by a company subject to US law remains exposed to the CLOUD Act, even if its servers are in Europe. The datacenter's location is not enough: what matters is who controls the data, and under which law. For a profession bound by professional secrecy or a business handling sensitive data, this is not a detail.
We covered this mechanism in why "hosted in Europe" is not enough, and the compliance side in cloud assistants and the GDPR.
What neutralizes it: getting out of the dependence on a foreign operator, ideally by hosting the AI on your own infrastructure, where no outside order has a recipient.
A cloud AI your organization relies on is a single point of failure you don't control. A price change, an acquisition, a service shutdown, and the tool disappears or doubles in price without notice. For an SME, rebuilding around a new solution in a hurry is expensive, in money and in time.
What neutralizes it: an instance that belongs to you and keeps running whatever happens to its vendor. If the provider shuts down, your assistant doesn't.
The three risks share the same root: your data and your tool live at someone else's place. Bringing them home settles all three at once. That is the principle of a sovereign AI deployed on your own infrastructure: your data doesn't leave, no foreign law applies, and no one can cut off your access.
This is exactly what Gungnir does: the assistant that installs on your premises, model-agnostic, with auditable code. Sovereignty there is not a sales pitch, it follows from where your data lives.
The risk is no greater than for a large group, but an SME has fewer means to keep it in check and just as much to lose. A customer data leak or the loss of a central tool weighs heavily when you have neither a lawyer nor a security team in-house.
It depends on each platform's terms. Several consumer services allow themselves to reuse inputs to improve their models. You have to check case by case, or choose a solution where the question doesn't arise because nothing leaves your premises.
By choosing a solution designed from the start to be hosted on your premises, with deployment support. You keep your data, your keys and your instance, without having to build an infrastructure team.
