AI and Compliance: 7 Questions to Ask Before Any Business Deployment

A team sees an impressive demo. Within minutes, everyone wants to test it with real data. That is exactly when the real questions begin: can we upload client documents? Candidate CVs? Contracts? Medical requests? These are not only technical questions — they are compliance questions. And they need to be asked before AI is connected to a live process, not after the first incident. Here are the seven questions that separate a controlled project from one that quietly exposes the business.

What data will the AI use?

The first question is the easiest to phrase and the most often skipped: which exact categories of data will the AI see? Anonymous internal text does not carry the same obligations as personal or sensitive data — names, contact details, CVs, HR records, health information, legal documents. The moment personal data is involved, the GDPR applies: a defined purpose, a legal basis, data minimisation, a retention period. Unclear data produces unclear risk. If no one can say precisely what the AI reads, no one can assess what it exposes.

What is the AI allowed to do?

An AI system does not have a single level of risk: everything depends on the action it is given. Summarising an internal policy is not the same as sending advice straight to a client, screening applications, or rejecting a file. So two things must be defined: the AI's role, and what it must never do. Can it draft something for approval, or act on its own? Can it send a message to the outside world, or only prepare internal content? The more an action affects a person, the more clearly the limit needs to be written down.

Does this use case create high risk?

Some areas call for stronger controls by nature: recruitment, HR, healthcare, legal, finance, personal safety. These are cases where an error or a bias does not just cost time — it can affect someone's rights, employment or safety. The EU AI Act follows exactly this logic: it is risk-based. The higher a system's impact, the stronger the obligations for transparency, documentation and oversight. Classifying a use case honestly — everyday use or high-impact use — sets the tone for everything else.

Who stays responsible, and who oversees?

When AI produces an output used in a decision, who answers for it? The answer is never the tool. Clear ownership must be assigned: a process owner, a data owner, a decision owner (in a smaller company, one person may hold several of these). That responsibility then turns into concrete human oversight: who reviews outputs, which ones require formal approval before they are used, and what happens when there is doubt or an error? Oversight should match the risk — light for an internal summary, strict for a decision that affects a person.

What does the vendor promise in writing?

Many guarantees are given in a sales call and quietly disappear afterwards. What matters is what sits in the contract. Where is the data hosted? Is it used to train the vendor's models? How long is it kept, and how is it deleted? Is there a data processing agreement (DPA) and a clear list of subprocessors? A business deploying AI on real data needs written, verifiable answers, not verbal reassurance. If a vendor cannot put it in writing, that is already an answer.

How will the system be monitored after launch?

An AI deployment is not finished on go-live day. A system's behaviour changes over time: usage drifts, errors appear, the sources it relies on get updated, an unforeseen edge case shows up. So decide from the start how you will track usage, errors, drift and source updates, and how an incident is escalated. Without that monitoring, a business only discovers problems once they have become visible from the outside — at the worst possible moment.

Where BeLogic fits

At BeLogic, we deploy AI agents around a clear use case, approved data sources, a defined human review, visibility over sources and logging of actions. In practice: an agent that prepares drafts for approval rather than a system that decides on its own; scoped data hosted in the EU; written limits on what the agent can and cannot do; a vendor that commits in the contract. We also help clients position their project against the GDPR and the AI Act, and access regional AI subsidies. The aim is not to slow AI down, but to make it both useful and controlled — because a system you can explain is a system you can rely on.