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 let an AI tool summarize internal reports without questioning what happens to this data. The result is often so-called shadow AI, tools that have not been 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 off-limits, 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 personal life and uses it independently for work. This initiative is generally positive but carries a risk. Without clear rules, sensitive information often ends up in applications whose data processing has not been vetted.

GDPR applies regardless of which tool is used. Anyone 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 effect, a second layer is added. In addition to GDPR, companies will also need to classify, which risk category their AI use falls into. For most office applications, such as text summarization or research, the risk is low. Nevertheless, it is worth taking a look at the current requirements before rolling out new tools company-wide, especially when AI systems make or prepare decisions about individuals, such as in hiring 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 should not go into an AI tool without prior review. Specifically, the following categories pose the greatest risks.

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

Health data is classified as a special category of personal data under Article 9 of 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 data in the strict sense, but they pose an immediate threat to competitiveness in the event of a data leak, for example, if pricing calculations or contract terms are disclosed to 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 then potentially outside their control and should ideally be replaced immediately once the incident is noticed.

A simple rule of thumb helps in everyday work. For every input, briefly check whether the information should 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 GDPR, usually the legitimate interests of the company or the consent of the affected person. Additionally, every AI provider 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 problems that are difficult to resolve. Especially in smaller teams, it helps to incorporate this review as a fixed step in the onboarding process for new software instead of leaving it to chance.

What to Consider When Choosing 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, typically store inputs 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’s 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 like ISO 27001 or SOC 2? Can the zero-data-retention option be contractually secured? A vendor-neutral assessment helps select the right tool based on your own infrastructure, compliance requirements, and budget, rather than relying solely on the provider's popularity.

For particularly sensitive areas, private deployments or locally hosted models may be appropriate. These solutions require more effort to set up, but give companies full control over their AI data processing. However, for many SMEs, a well-configured enterprise solution with contractually secured data processing is often sufficient; complex self-hosting usually only pays off at a certain data volume or with particularly strict compliance requirements.

How Artificial Intelligence is Safely Integrated into Companies

Technical Measures for Safe 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 operate via API instead of copy-pasting in the browser tool, as API accesses can be secured and logged more granularly. Additionally, it is worthwhile to regularly review which employees still need access to which AI tools, so that permissions do not grow unchecked over the years.

Organizational Measures and Employee Training

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 test and adjust the policy in practice before company-wide implementation.

When artificial intelligence is introduced 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 departments jointly support the policy instead of treating it as a mere IT mandate that quickly gets forgotten in day-to-day operations.

What AI Compliance Means in Practice

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

AI compliance can be distilled into a short checklist, which can be implemented in most companies within a few weeks. The order of the points is more important than the number: Those who clarify contractual safeguards first and only then decide on tools and training save themselves from 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 your own implementation.

The secure use of AI is an ongoing task. It starts with the legal basis, continues with the selection of providers, and culminates in a lived policy. Companies that consider 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?