‍GDPR-compliant AI for businesses is possible, but not with every AI product in every tariff level. It is not the model name alone that is decisive, but which contract and data protection conditions apply in the corporate context, how data is processed and which controls you establish technically and organizationally.
An important definition in advance: “GDPR-compliant” does not mean “automatically secure.” It means that as a company, you demonstrably meet the requirements of the GDPR, including legal basis, transparency, purpose limitation, data economy, security (TOMs), data subject rights and accountability.
With generative AI, there is an additional reality check: It is not enough for the provider to say “we are compliant.” You need reliable answers about data flows, order processing, subcontractors, storage periods, training usage, data residency, and admin controls.
This is exactly where consumer tools separate from business and enterprise offerings. And this is exactly where the difference between shadow IT and a robust AI strategy comes in.
GDPR-compliant AI for companies: What “compliant” really means
For GDPR-compliant AI for businesses What you need is clarity about roles and duties first. In many setups, you are the responsible person, the provider is a contract processor, and other service providers are subcontractors.
That sounds formal, but it's highly practical: Only when this chain is clean can you actually enforce requirements such as deletion, information or documentation.
Typical minimum requirements include an AV contract/DPA, clear purpose definition, access controls, logging, encryption, and a procedure for incidents.
As soon as you put personal data or sensitive company data into prompts, files, or connectors, you're right in the middle of data protection and information security. “Just a few lines of text” is enough for a risk if it contains customer data, employee data, or trade secrets.
A good principle: You don't have to ban every AI feature, but you need to design traffic so that it's auditable, limited, and controlled.
For a practical classification of whether and when AI models themselves can become relevant as “personal”, it is worth taking a look at the current orientation of the European data protection supervisory authority on AI models and personal data (European Data Protection Board (EDPB): Artificial intelligence).
From when are you really compliant with data protection regulations?
With GDPR-compliant AI for businesses The product level is often more important than the model name. “ChatGPT” can mean something completely different depending on the context of use: Consumer, Business, Enterprise, or API.
The same applies to Microsoft Copilot: In terms of data protection, a consumer co-pilot is rated differently than Microsoft 365 co-pilot in a tenant with Enterprise Controls. And Gemini is not the same as Gemini: Gemini in Google Workspace is subject to different promises than consumer use.
For companies, the key question is therefore: Which exact promises apply contractually and technically to my use?
A decisive criterion is whether inputs and outputs are used as standard for model training or not. For business and enterprise offerings, “no training by default” is often a key promise today, but you should always check it in the respective enterprise privacy documentation.
OpenAI, for example, highlights for business data in certain business products and the API that it is not used for training by default (OpenAI: Enterprise privacy).
This is a strong basis, but it does not replace your own obligations: You must continue to regulate purpose, legal basis, data minimization and internal guidelines cleanly.
Classify ChatGPT correctly
When companies have GDPR-compliant AI for businesses Speaking, ChatGPT almost always ends up first on the list. The key point is: Do you use a company variant with admin and data protection functions, or do you use consumer accounts?
In a corporate context, these points are typically relevant: training use of content, data storage, SSO and identity, admin controls, audit logs, connector authorizations and contractual arrangements for order processing.
Connectors and “company knowledge” in particular are exciting because they bring benefits but also increase authorization and data flow risks. Then you need role-based access and clear authorization logic.
For enterprise and Edu contexts, OpenAI describes, among other things, that company data is not used for training by default and that access rights to corporate knowledge respect existing authorizations (OpenAI Help Center: ChatGPT Enterprise & Edu Release Notes).
In practice, this means that ChatGPT can be part of a GDPR-compliant AI strategy if you combine enterprise controls, clear guidelines and data classification and prevent or strictly restrict consumer use in the company.
Microsoft Copilot privacy check
Microsoft 365 Copilot is for GDPR-compliant AI for businesses interesting because it is deeply integrated into Microsoft 365 and thus covers many use cases: email, documents, teams, meetings, search.
The advantage is also the biggest data protection task: Copilot accesses data stored in the tenant. As a result, authorization management suddenly becomes AI governance. If SharePoint is wild, Copilot will make it visible.
Terms such as “Enterprise Data Protection”, Data Protection Addendum, Tenant Controls, Logging, and Data Residency are important here.
Microsoft describes that Enterprise Data Protection addresses GDPR support and EU Data Boundary, among other things, and that data should only be used in accordance with instructions (Microsoft Learn: Enterprise data protection).
In addition, Microsoft explains the EU data boundary and the routing of LLM calls within these limits for EU traffic for Microsoft 365 Copilot (Microsoft Learn: Data, Privacy, and Security for Microsoft 365 Copilot).
For your rollout, this means: With Copilot, not only is “configuring AI” crucial, but above all “cleaning M365”: rights, sensitivity labels, DLP, external approvals, guest accounts, and a governance concept for SharePoint and teams.
Check Gemini and Google Workspace
Gemini is often introduced in companies via Google Workspace, and that is exactly where the question of GDPR-compliant AI for businesses.
The key question is: Is workspace customer data used for training purposes and which admin controls apply? Also relevant: data region, logging, access by support, and the question of which data ends up in prompts or file attachments.
Google provides Gemini in Workspace with its own Privacy Hub, which is intended to explain how to use and protect data in Workspace with generative AI (Google Workspace Admin Help: Generative AI in Google Workspace Privacy Hub) . (Google help)
For companies, a clear rule of action can be derived from this: If Gemini, then via the workspace account with admin control, not via private consumer variants. And you need clear policies as to which document types can actually be processed in AI functions.
An additional tip from practice: Check smart features and personalization settings centrally. Even though this does not automatically mean “training,” it influences which data is used for functions and how users perceive control. (The Verge)
When is which model “compliant”
Many are looking for a list such as “starting with version X, it is GDPR-compliant.” For GDPR-compliant AI for businesses Unfortunately, this is too short-sighted because “compliance” depends on four factors:
First: product level and contract (consumer vs business/enterprise, DPA/AVV, subcontractor, third-country transfer).
Second: Technical protection measures (encryption, logging, admin controls, data residency, deletion processes).
Third: Your use case (support draft without personal data is different from HR selection processes or health data).
Fourthly, your organization (policies, training, approvals, DPA, incident processes).
Therefore, the useful comparison is not “model A vs model B” but “operating model A vs operating model B”. An enterprise offering with “no training by default”, clean AVV and EU data boundary is closer to a GDPR-compatible operation than a consumer tool without administration.
If you also want to consider EU regulation in addition to data protection: The EU AI Act is rolling out gradually and, depending on the type of system, entails additional obligations, regardless of GDPR issues. Among other things, the European Commission mentions key dates for general-purpose AI obligations and governance (European Commission: EU AI Act implementation timeline).
GDPR-compliant AI for companies: What companies need to consider in concrete terms
When you GDPR-compliant AI for businesses If you want to actually implement, you need a set of must-haves. These points are the most common pitfalls in projects:
You need data classification that employees can actually use. Without clear classes such as “public, internal, confidential, strictly confidential,” you can't formulate useful rules.
You need clear prompt and upload rules: What is allowed in, what never? In particular, personal data, special categories and trade secrets belong to clear “no-go” or “only under conditions” categories.
You need an AVV/DPA and an evaluation of third-country transfers. This applies not only to the AI provider, but also to subcontractors, monitoring, support and, if applicable, connector services.
You need a deletion and storage concept: How long is content stored, how do you delete it, and how do you implement data subject rights?
You need security-by-design: MFA/SSO, conditional access, DLP, encryption, role-based rights, and an authorization concept for connectors.
And most importantly: You need training. Not as a mandatory video, but as a concrete “do and don't” guide with examples from your use cases.
As a helpful guide to how data protection authorities see AI development and deployment structured, the current recommendations of the French supervisory authority on GDPR-compliant AI system development are very practical (CNIL: Recommendations to comply with GDPR).
This is how you proceed strategically
A strategic start for GDPR-compliant AI for businesses Rarely fails due to technology. It fails because companies get started without use case prioritization and without governance.
A six-step approach that quickly delivers benefits and controls risks has proven effective.
Step 1: Collect and prioritize use cases. Focus on high-volume processes and clear quality criteria, such as internal knowledge search, design preparation, summaries, assistance in standard communication.
Step 2: Assign data and risk classes. Which data flows? Are there any special categories? Are there any works council issues? Which systems are involved?
Step 3: Define operating model. Decide whether you want to use SaaS with Enterprise Controls, prefer an EU-hosted alternative, or need hybrid/on-prem.
Step 4: Clarify law and security in parallel. AVV/DPA, TOMs, transfer impact assessment, logging, deletion concept, role and rights concept.
Step 5: Pilot with guardrails. Small user group, clear prompt policy, monitoring, feedback loops, defined acceptance criteria.
Step 6: Scale with Enablement Training, self-service templates, internal AI policy, change management, and a central location for exceptions.
This allows you to achieve effect quickly without promoting “Shadow AI.” And you build a foundation on which you can run more sophisticated AI agents and automations later on.
Choose the right AI infrastructure
The infrastructure issue determines how well GDPR-compliant AI for businesses works in the long term. Many companies choose “a tool,” but actually need an AI platform with clear guidelines.
There are roughly four infrastructure paths that have proven effective in projects.
Path A: SaaS Enterprise (ChatGPT Business/Enterprise, M365 Copilot, Gemini Workspace) Fast, strong, but you have to have a good command of contract, data flow, admin controls and policies.
Path B: EU hosting/sovereign options. Often useful when data residency, government or KRITIS requirements, or customer contracts are stricter. Data center location, key management and subcontractors are particularly important here.
Path C: Self-hosted or on-prem LLM. Useful for very sensitive data or when you want maximum control. In return, operating costs, model maintenance, monitoring, cost control and safety responsibility are increasing.
Path D: Hybrid: Sensitive data locally, less critical use cases in SaaS. In practice, this is often the best compromise if you want to use them quickly and yet minimize data risks.
When choosing, you should use a simple decision matrix: data classes, latency, cost per request, integrations, admin functions, auditability, and compliance features. And you should always test how well the provider really supports deletion, export, and logs.
A typical criterion that many overlook: Prompt and output logging can itself be critical under data protection law. You want to audit, but you don't want to save content unnecessarily. Pseudonymization, sampling and clear retention rules help here.
GDPR-compliant AI for companies: checklist for purchasing and IT
When you GDPR-compliant AI for businesses If you buy or introduce, a checklist that purchasing, IT, security and data protection share helps.
Ask for an AVV/DPA, a list of subcontractors, storage locations, data residency options, and a clear statement about training usage.
Ask about admin controls: SSO, roles, rights, connector authorizations, multi-client capability, logging, export, deactivation of individual functions.
Question about security: encryption in transit and at rest, key management, incident processes, certifications, pen testing, and support access.
Question about the data life cycle: standard retention, deletion periods, deletion mechanisms, storage in backup, and how data subject rights are supported in practice.
And ask about governance features: policies, policy enforcement, DLP integration, sensitivity labels, and ways to restrict uploads.
If you get these points answered correctly, you are already much closer to a reliable decision than with a “feature comparison.”
GDPR-compliant AI for companies: FAQ
Is ChatGPT automatically GDPR-compliant if it's a business product?‍
No, it is never automatic. Business/Enterprise can provide a better foundation, but you still need AVV/DPA, internal policies, data classification, DSFA when needed, and technical controls.
Is Microsoft Copilot more privacy-friendly because it's integrated with Microsoft 365?‍
Integration is an advantage, but it is also a risk. Copilot makes existing permissions effective. Without clean rights, DLP and labels, unwanted visibility can occur.
Can I use Gemini in companies in a GDPR-compliant manner?‍
Yes, if you regulate usage via Google Workspace with admin control, review contracts and establish clear policies. You shouldn't allow consumer use as a standard in your company.
Do I always need a data protection impact assessment (DSFA)?‍
Not always, but often when there are higher risks, such as extensive processing of personal data, profiling, HR decisions or special categories. In practice, a preliminary check is almost always worthwhile.
What is the most common mistake with GDPR-compliant AI?‍
Shadow IT. When teams use consumer tools because it “goes faster,” you lose control over data flows. A good business solution plus clear rules is usually the better and even faster option.
GDPR-compliant AI for companies: Conclusion and next step
GDPR-compliant AI for businesses is not a single tool, but a combination of product level, contracts, technical controls and governance. ChatGPT, Microsoft Copilot, and Gemini can work in a corporate context if you run them as an enterprise setup and not as an uncontrolled consumer tool.
The biggest lever rarely lies in the model, but in your organization: data classification, clear policies, clean authorizations, DSFA logic, retention rules and enablement for employees.
If you approach the topic in a structured way, you will quickly get added value without data protection roulette. And you create an infrastructure that can also support future data protection and EU regulation requirements.
If you want to implement AI in your company in a privacy-compliant and pragmatic way, we as an AI company are happy to provide non-binding advice - from use case selection to governance and tool comparison to the appropriate AI infrastructure. Contact us anytime if you're looking for a clear, secure start.‍
