How companies can use AI in compliance with GDPR in everyday life

AI tools are already in use in many companies, often faster than the data protection department can react. Employees copy customer data into ChatGPT, upload contracts into a language model, or have an AI tool summarize internal reports without asking what happens to this data. The result is often so-called shadow AI, tools that no one has officially approved but are still used daily. This article shows how to use AI in compliance with GDPR in your everyday life, which data is taboo, and which technical and organizational measures really help.

Why many companies use AI without knowing the GDPR risks

The first contact with generative AI rarely happens through the IT department. Usually, a single team member brings the tool from their private life and uses it independently for work as well. This initiative is fundamentally positive but carries a risk. Without clear rules, sensitive information often ends up in applications whose data processing has not been checked by anyone.

The GDPR applies regardless of which tool is used. Anyonewho inputs personal data into a free AI application, without legally securing the processing, risks fines, reputational damage, and in the worst case, the loss of trade secrets. At the same time, practice shows that a complete ban rarely achieves its goal because employees still use the tools, just uncontrolled and without any safeguards. Clear guidelines work much better than a blanket ban because they provide employees with a safe framework within which they can work productively instead of pushing them into uncontrolled shadow use.

Since the EU AI Act is gradually coming into force, a second level is added.In addition to the GDPR, companies must also categorize, the risk category of their AI use. For most applications in office life, such as text summarization or research, the risk is low. Nevertheless, it is worth taking a look at the current requirements before new tools are rolled out company-wide, especially when AI systems make or prepare decisions about individuals, such as in applications or credit approvals.

Which data you should never input into an AI tool

The most important rule is easy to remember but hard to maintain in everyday life. Anything you wouldn't post on an open bulletin board in the office also doesn't belong in an AI tool without prior review. Specifically, the following categories are among the greatest risks.

Customer data such as names, addresses, contract numbers, or order details are considered personal data and may only be processed if there is an appropriate legal basis and a data processing agreement with the AI provider. Employee data such as salary information, sick leave notifications, or application documents are subject to the same strict rules and deserve even more cautious handling, as the trust relationship between the company and the workforce is also at stake.

Health data is classified as a special category of personal data under Article 9 of the GDPR and requires additional protective measures that most standard AI tools do not meet.Financial data, contracts, and internal calculations may not always fall under personal datain the strict sense, but they directly jeopardize competitiveness in the event of a data leak, for example, if pricing calculations or contract conditions reach third parties.

Source code and technical documentation are often underestimated. Anyone who inputs proprietary code into an AI tool for debugging that stores inputs for training purposes may inadvertently disclose trade secrets to a third party. A realistic example: A developer copies a faulty code snippet including internal API keys into a public AI tool to get help with debugging. The key is thus potentially outside of their control and should ideally be replaced immediately once the incident is noticed.

A simple rule of thumb helps in everyday work. Withevery input, briefly check whether the information should also be known outside the company. If not, it should initially be anonymized, partially removed, or not input into the tool at all.

How to use AI in compliance with GDPR in your company

Using AI in compliance with GDPR starts with the legal basis, even before the technology. For every processing of personal data, there needs to be a legal basis under Article 6 of the GDPR, usually the legitimate interests of the company or the consent of the affected person. Additionally, every AI provider used that processes data on behalf needs a data processing agreement.

In practice, this means that before a team uses a new AI tool, it should be clear which categories of data will be processed, whether a data processing agreement is in place, and who in the company grants approval. These three questions can be clarified in a few minutes and prevent most of the later cumbersome problems. Especially in smaller teams, it helps to incorporate this check as a fixed step in the onboarding process for new software instead of leaving it to chance.

What to consider when selecting AI providers for data processing

AI data processing varies greatly depending on the provider and contract model.Consumer versions like the free version of ChatGPT store inputs by default for training purposes, unless the setting is actively disabled. Enterprise and API versions, on the other hand, often offer zero data retention. Inputs are not stored after processing and do not contribute to the training of future models.

When selecting a provider, it is worth looking at the following points. Is there a data processing agreement in place? Where is the data processed, inside or outside the EU? Are there certifications such as ISO 27001 or SOC 2? Can the zero-data-retention option be contractually secured? A vendor-neutral evaluation helps to select the appropriate tool based on one's own infrastructure, compliance requirements, and budget, rather than relying solely on the popularity of a provider.

For particularly sensitive areas, private deployments or locally hosted models are an option. These solutions require more effort in setup,but give companies full control over their AI data processing.For many medium-sized companies, however, a well-configured enterprise solution with contractually secured data processing is usually sufficient; elaborate self-hosting is often only worthwhile from a certain data volume or with particularly strict compliance requirements.

How artificial intelligence is safely integrated into companies

Technical measures for secure AI integration

Technically, much can be secured before any employee works with an AI tool. Role-based access rights ensure that only authorized persons have access to sensitive data sources. Logging and monitoring of usage create traceability in case an incident occurs. Where possible, interfaces should run via the API instead of copy-pasting in the browser tool, as API accesses can be secured and logged more granularly. Additionally, it is worth regularly reviewing which employees still need access to which AI tools so that permissions do not grow uncontrolled over the years.

Organizational measures and training of employees

Technology alone is not enough. An internal AI policy defines which tools are allowed, which data may be processed, and who approves new tools. Regular training ensures that these rules are also implemented in everyday life and are not just posted on the intranet. A gradual rollout, starting with a pilot team, helps to test and adjust the policy in practice before it is introduced company-wide.

When artificial intelligence is introduced in companies in this way, with clear technical guidelines and a lived policy, the risk of data breaches is significantly reduced without compromising productivity. It is crucial that management, IT, and specialist departments jointly support the policy instead of treating it as a mere IT directive that quickly fades from memory in day-to-day operations.

What AI compliance means in practice

AI compliance can be broken down into a short checklist that can be implemented in most companies within a few weeks.

AI compliance can be broken down into a short checklist, which can be implemented in most companies within a few weeks. The number of points is less important than the order: Those who first clarify the contractual safeguards and only then decide on tools and training save themselves cumbersome corrections later.

In practice, AI compliance rarely fails due to a single missing point but rather because technical and organizational measures are thought of separately. A company with clean access rights but no internal policy is just as vulnerable as one with a policy that no one knows. The following checklist brings both levels together and can be used directly as a starting point for one's own implementation.

This checklist does not replace individual legal advice but providesguidance on how most AI compliance questionscan be clarified in everyday corporate life.

Data-secure use of AI is an ongoing task. It starts with the legal basis, continues through provider selection, and culminates in a lived policy. Companies that think about these three levels early on save themselves later cleanup work and can leverage the benefits of AI without starting from scratch with each new application.

Do you want to use AI securely in your company but don't know where to start?