Using AI Safely: ChatGPT, Confidential Data, and Shadow AI at Work

Risk Analysis in Daily Work: How Shadow AI Emerges

The temptation is omnipresent in the modern daily work routine: quickly draft an email, debug complex program code, or summarize a long, confusing PDF document. However, when employees take matters into their own hands and use generative AI tools without explicit approval from the IT department, they create uncontrolled shadow AI within the organization. The primary risk associated with this behavior is the uncontrolled leakage of business-critical information and intellectual property. Most free versions of online AI services use the entered prompts by default to train their own underlying models. Anyone who copies confidential customer data, internal financial reports, or proprietary source code into such a tool is unintentionally giving away this valuable data to external third parties and losing all control over its future use. For the broader process, see EU AI Act: What AI Literacy Under Article 4 Means for Companies. The information security topic area places these rules in the wider security context.

A concrete practical example illustrates this associated risk clearly. A software developer wants to fix a bug in a proprietary algorithm and copies the affected code snippet into a freely accessible AI model on the internet. Without realizing it, they have uploaded the company's protected trade secrets to a third-party server, where they can be analyzed for future model updates and potentially presented to other users. A similar situation occurs in customer service when impatient employees input customer inquiries containing real names, addresses, and contract details to generate a polite response. To effectively prevent such high-impact incidents in daily work, companies must raise security awareness and define clear usage rules. A useful deeper dive is GDPR Training for Employees: Content, Timing, and Evidence.

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Data Protection Guardrails: GDPR and Personal Data

The processing of personal data is subject to strict regulations in Europe under the General Data Protection Regulation. If customer data, employee data, or other identifiable information is transferred to external AI providers without a valid legal basis, this constitutes a serious violation of the GDPR. It is therefore essential for companies to examine the legal framework very closely before establishing any new tool within the team. Simply clicking "agree" when registering a free account is by no means sufficient to meet the requirements for lawful data processing. This is where a well-founded DSGVO-Grundlagen course helps to anchor the necessary basic knowledge within the team in a sustainable manner and avoid legal pitfalls in handling personal data beforehand.

To legally process personal data in AI systems, companies must generally conclude data processing agreements (DPAs) according to Article 28 GDPR with the respective providers. However, many standard consumer tools do not offer this option at all, or only in their more expensive enterprise tiers. Furthermore, it must be contractually ensured that the data is not used for training the models and that any transfer of data to third countries, such as the USA, is based on solid legal foundations like the EU-US Data Privacy Framework. Without these contractual assurances, business use is associated with significant legal risks and potentially high fines. The responsibility always lies with the data-processing company, which must be able to demonstrate compliance with data protection principles at all times.

The EU AI Act and Its Impact on Businesses

In addition to the established requirements of the GDPR, the new European legal framework for artificial intelligence, known as the EU AI Act, sets completely new standards for compliance. This regulation follows a risk-based approach, categorizing AI applications into different risk classes ranging from minimal risk to unacceptable risk. While certain practices such as social scoring are completely prohibited, strict documentation, transparency, and security requirements apply to high-risk systems. However, even for supposedly low-risk applications such as general text generators, companies must ensure certain transparency obligations and, above all, the legally required "AI literacy" of their employees. A structured EU AI Act course provides the ideal foundation for understanding these statutory requirements in detail and implementing them in a legally compliant manner within your own operations. For deeper training, EU AI Act course is a useful fit.

The obligation to promote AI literacy means specifically that employers must ensure their employees are capable of operating AI systems safely, critically, and responsibly. This includes recognizing AI-generated hallucinations, understanding the basic functioning of the models, and knowing the limitations and potential biases of the technology. Neglecting these training obligations not only risks serious security incidents due to misinformed decisions but also violates the regulatory requirements of the EU AI Act in the medium term. Implementing systematic training programs is therefore no longer an optional luxury, but a mandatory regulatory necessity for modern organizations in the digital age.

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Practical Protective Measures: Data Classification and Prompting Rules

To remain operational in a dynamic daily work routine, a general, rigid ban on AI tools is rarely helpful; instead, a structured and conscious approach to data handling is required. The first and most important step in this process is a clear and understandable Informationsklassifizierung within the company. Employees must know exactly which data categories exist (e.g., public, internal, confidential, strictly confidential) and which of them may be shared with external tools at all. While publicly accessible information or general marketing texts can be processed without hesitation, internal documents, personal data, or trade secrets require the highest level of confidentiality and must never be entered into unsecured, public AI systems.

In addition to pure classification, learning safe prompting techniques is a critical success factor. Employees should be systematically trained to formulate prompts in such a way that no conclusions can be drawn about sensitive company data. For example, instead of prompting: "Write an email to our client John Doe from company XYZ, who is complaining about the delayed delivery of 500 servers," the prompt should be: "Write a professional apology email to a business client regarding a delivery delay for hardware components." Through this conscious abstraction, the utility of the tool is maximized while the risk of data leakage is effectively minimized. These practical behaviors must be firmly integrated into daily workflows.

Establishing and Training the AI Policy in the Company

A written policy is the indispensable foundation for the safe use of AI in any organization. It defines clear rules on which tools are permitted, how data should be handled, and who serves as the point of contact for questions or uncertainties. However, a policy that merely gathers dust as a dry PDF document on the intranet has no real impact in practice. It must be actively trained, understood, and lived. With a dedicated KI-Tools sicher nutzen Kurs course, companies can ensure that all employees are able to apply theoretical security rules safely even in a hectic daily work routine and develop a genuine understanding of the risks.

Since every company has individual requirements, utilized tools, and specific risk profiles, a standard off-the-shelf course is often not sufficient to cover all internal nuances. It is therefore highly recommended to integrate specific internal guidelines and approval processes directly into the training. Modern platforms allow you to map your eigene KI-Richtlinie als Kurs and adapt it flexibly. This significantly increases employee acceptance, as they are trained directly on the tools and processes relevant to their specific daily work. The combination of theoretical security knowledge and the practical application of internal guidelines drastically minimizes the risk of shadow AI and high-impact data breaches.

Technical Controls and Secure Alternatives

In addition to organizational measures and continuous training, technical controls play a crucial role in risk minimization. Companies should provide their employees with secure, officially approved alternatives to public consumer tools. This can be achieved, for example, by providing enterprise licenses where it is contractually guaranteed that entered data is not used for model training and that server locations comply with European data protection standards. Controlled access via secured APIs, which can be monitored by the IT department and equipped with appropriate filters for sensitive data, is also a proven method to ensure security within the company.

However, technical blocks alone often fall short, as they restrict the creativity and productivity of employees, thereby increasing the incentive for shadow AI and bypassing security barriers in the first place. The best path forward is a balanced combination of secure, easily accessible tools, clear guidelines, and continuous, interactive education. Companies that choose this holistic approach benefit from the enormous productivity advantages of generative AI without taking on incalculable risks to data protection and information security. To take the first step and build your company's compliance infrastructure, you can Start free trial on our platform and experience the flexible, practical training options for yourself. For a real trial run, teams can Start free trial.

Related ConformBase pages

• KI-Tools sicher nutzen Kurs
• EU AI Act course
• DSGVO-Grundlagen
• Informationsklassifizierung
• eigene KI-Richtlinie als Kurs
• Start free trial

Official sources

• EU AI Act
• GDPR

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