Data protection with AI: What companies need to consider!

Data protection in AI applications is no longer a side issue, but a central requirement for the responsible use of artificial intelligence in companies. AI lives on data — and therefore very often on personal data from customers, employees or applicants. Anyone who takes a careless approach or uses “free” tools without verification risks legal violations, damage to reputation and loss of trust.

• Why data protection is so important for AI applications
• Why the rule of thumb “If something is free, we are the product” applies in an AI context
• What risks arise from “shadow AI” and unregulated use
• Why companies need clear rules, processes and responsibilities
• How to evaluate AI platforms in a structured way from a data protection perspective

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Data protection in AI does not mean blocking innovation, but creating framework conditions: Which data can be used, which cannot? Which tools are approved? How are risks assessed and documented? Companies that answer these questions early on can use AI more securely and sustainably.

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Data protection in AI: Why the issue is so critical

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Data protection in AI is particularly sensitive because AI systems process large amounts of data, link them together and derive patterns from them. In many cases, this involves personal data — from emails to chat histories to movement and usage data. The GDPR provides the legal framework in Europe: All processing requires a legal basis, must be earmarked, transparent and limited to what is necessary.

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In addition, there are new regulations such as the European AI Act, which classifies certain AI applications (e.g. in an employment context or for scoring) as “high-risk” and defines additional requirements. German data protection supervisory authorities have already published detailed guidelines for the privacy-compliant operation of AI systems — including technical and organizational measures.

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From a company perspective, data protection in AI is also a factor of trust: Customers and employees expect that their data does not flow uncontrollably into external AI models. If sensitive content is accidentally copied into insecure tools, this can quickly become a compliance case — and in extreme cases, fines or loss of contract.

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The rule of thumb is “When something is free...”

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The well-known rule of thumb “When something is free, we are the product — or our data” describes a common business model of digital platforms very aptly: Payment is not made with money, but with attention and data that is used for advertising, profiling or other purposes. In this form, the sentence is based on a post from the user “blue_beetle” from 2010 and has been quoted frequently since then.

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In the context of data protection and AI, this rule is particularly important: Many AI tools are publicly available and seemingly “free of charge.” The return often consists in the fact that entered content can be used to train or improve models or is stored for analysis and tracking purposes. Anyone who inserts confidential business documents, customer data, or source code into such tools may reveal more than they know.

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At the same time, it is important to understand that free does not automatically mean data hostile — but it is a warning sign. Reputable providers transparently explain whether inputs are used for training purposes, how long they are stored and what options companies have to limit this (e.g. “no training” modes or enterprise plans). Data protection in AI therefore starts with the question: What does this product live on economically - and what role does our data play in it?

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Data protection and AI in companies: Clear rules for employees

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“Shadow AI” is a central risk in everyday life: Employees use privately known AI tools in a work context without IT or data protection officers knowing about them. Recent surveys show that a large proportion of employees are already using generative AI at work — many of them without approval or clear guidelines. In studies, numerous users also claim to enter sensitive data such as customer data, internal documents or source code into unauthorized AI tools.

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Data protection in AI can therefore hardly be guaranteed without clear corporate rules. Organizations should at least:

• define Which AI tools are allowed, restricted, or prohibited are
• explain, Which types of data never may be entered into external tools (e.g. special categories of personal data, trade secrets),
• define, Who issues approvals and how new tools are tested
• and employees regularly sensitizehow they can use AI safely.

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Without such guidelines, employees often act well-intentioned but legally risky. A clear, understandable guide lowers the hurdle of asking questions before copying data into a tool — and significantly reduces the likelihood of data protection incidents.

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Check data protection: Choose the right AI platform

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Not every AI platform is equally suitable from a data protection perspective. Companies should systematically check which solutions suit their requirements. Important audit questions include:

• Where is the data processed and stored (EU, EEA, third country)?
• Are inputs used for training purposes — and can this be switched off?
• Is there Order processing contracts (Art. 28 GDPR) and transparent security concepts?
• Which Role and rights models Is there so that not every person can see everything?
• How to become Logging, deletion concepts and data subject rights (information, deletion) implemented?

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Especially when it comes to “corporate AI” platforms (e.g. co-pilot variants, enterprise workspaces, local solutions), it's worth taking a closer look: Some offers allow dedicated EU regions, “no training” options or even on-premises operation, others rely on global clouds with data replication. Data protection in AI therefore also means that a Vendor selection with compliance glasses instead of just looking at functionality and price.

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For higher-risk scenarios — such as automated decisions with significant effects on people — an additional Data Protection Impact Assessment (DSFA) be necessary. Numerous guidelines recommend that DSFA obligations be checked early on, rather than just before they go live.

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Data Protection, AI and Law: GDPR, AI Act & Regulators

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Legally, the GDPR remains the central framework for data protection in AI: the legal basis (Art. 6 GDPR), transparency, data minimization, purpose limitation, storage limitation and technical/organizational measures also apply to AI applications. Automated decisions with legal effect or similar significant impairment are also subject to special requirements (Art. 22 GDPR).

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A new addition is the AI Act the EU, which is gradually coming into force. It classifies AI systems according to risk and sets requirements for documentation, transparency, risk management and human supervision, among other things. For employers and platform operators, there are additional guidelines which restrict or prohibit manipulative or discriminatory practices in particular - such as emotion tracking in the workplace or social scoring.

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German data protection supervisory authorities have also published specific information on the use of AI — including on technical and organizational measures and on generative AI. These documents give companies concrete clues on how to implement data protection in AI in practice, e.g. through clear responsibilities, logging, regular model reviews and training.

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Privacy and AI: Common Questions (FAQ)

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Can we simply use freely available AI tools in companies?

Not without testing. Even with freely accessible tools, the GDPR and, if applicable, industry rules apply. Companies should at least check the privacy policy and terms of use, clarify whether inputs are used for training purposes and whether an order processing contract is possible. Without this check, no personal or confidential information should be entered into the tool.

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Do we need to inform our employees about using AI?

Yes Transparency is a central principle in data protection. Employees should know which AI tools are used in the company, which data is processed and which rules apply. For certain deployment scenarios — such as profiling or automated decisions in the HR sector — additional information requirements and participation issues must be considered.

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When do we need a data protection impact assessment (DSFA) for AI?

Whenever processing is expected to be a high risk For rights and freedoms, data subject - e.g. in the case of extensive profiling, evaluation of persons or sensitive data. Guidelines on AI and data protection recommend that DSFA requirements be reviewed early on, particularly when it comes to HR analytics, scoring or automated rejection decisions.

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How do we handle the “When something is free...” rule of thumb internally?

The rule of thumb is very suitable for training: It reminds you to immediately ask about the business model when using free tools. Companies can include them in awareness materials and back them up with specific examples — such as social media platforms or “free” AI tools whose business model is heavily based on data use. It is important to make it clear that not every free offer is problematic, but every one should be consciously checked.

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Who should be internally responsible for data protection in AI?

Management or the person responsible within the meaning of the GDPR always remains responsible. Operationally, data protection officers, IT, specialist departments and, if applicable, compliance should work closely together. In many companies, a type of “AI Governance Board” is also being established, which sets guidelines, approves tools and supports critical projects.

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Is AI compliant with data protection regulations?

No — in any case, most AI tools aren't. Many AI tools are publicly available and seemingly “free of charge.” The return often consists in the fact that entered content can be used to train or improve models or is stored for analysis and tracking purposes. You should always read the fine print as well.

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Data protection and AI: Conclusion and outlook

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Data protection in AI is not a “nice-to-have”, but a basic requirement for sustainable use of AI. Anyone who processes personal data in AI systems is right within the scope of the GDPR and AI Act — with clear requirements for legal basis, transparency, security and governance. At the same time, it is clear that innovation and data protection can be combined well with the right preparation.

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For companies, this means in concrete terms: The rule of thumb “If something is free, we are the product” should become a reflex — especially when it comes to AI tools. Clear internal rules, tested platforms, training and technical protective measures reduce risks and prevent valuable data from migrating uncontrollably into external models.

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The KI Company helps companies plan and implement AI applications in compliance with data protection regulations - from tool selection to guidelines and training to integration into existing systems. If you would like to clarify how you can incorporate data protection with AI in your company in a structured way, feel free to contact us at any time without obligation.