Unfortunately, the continuous improvement and associated spread of artificial intelligence in both private and business environments does not only bring advantages. So-called shadow AI is increasingly posing challenges for companies. When employees use unauthorized AI applications without supervision, this creates a significant security risk. In the worst case, sensitive information flows into external tools, which can lead to data leaks, massive compliance problems, and/or data protection violations (GDPR). In this article, learn why shadow AI is highly critical and what approaches can help to detect and limit it at an early stage.
Translated with DeepL.com (free version)
What exactly is shadow AI?
Shadow AI (also known as shadow artificial intelligence) describes the use of artificial intelligence within a company without the relevant tools being officially reviewed and approved by the IT department, data protection or other responsible bodies. In other words, employees use AI (in whatever form) on their own initiative, outside of defined processes and without clear rules. This phenomenon is closely related to the well-known shadow IT, but goes one step further. AI not only processes data, but also generates content, makes suggestions, and influences decisions.
The widespread use of generative AI is considered the key driver of this development. Such applications can be used without any specialist knowledge and deliver texts, evaluations, summaries, and more in seconds. According to a recent study by the Nuremberg Institute for Market Decisions (NIM), approximately 30 percent of the total population in Germany already regularly uses the services of ChatGPT. Given these conditions, the step from private use to use at work is very small.
Shadow AI manifests itself in companies primarily in two typical situations:
1. On the one hand, employees may activate new AI functions in already approved software solutions without realizing that these enhancements require a new security or data protection review.
2. On the other hand, individual colleagues or sometimes even entire teams independently procure cloud-based AI services to make their work more efficient – without consulting IT, data protection, or compliance.
The IT department and data controllers often only find out about this once problems have already arisen.
A particularly common scenario for shadow AI involves the use of generative text and analysis tools – especially ChatGPT – via private user accounts in everyday working life. Content from internal documents, emails, or databases is copied without further ado and inserted for further processing. Very few people think about the possible consequences. Many assume that their input will not be reused. In practice, however, this data can be stored, evaluated, and/or used for training models.
Risks of shadow AI: Why is shadow AI so dangerous?
Shadow AI puts companies in a situation that is easily underestimated. The uncontrolled use of artificial intelligence can have serious consequences, both technically and legally and economically. Only those who are aware of the risks can take targeted countermeasures. Without this awareness, dangers often arise insidiously – and only become visible once the damage has already been done.
Data protection violations and security gaps
A particularly sensitive area is (of course) the handling of critical data. As soon as employees use AI applications without clear rules, companies lose control over what information is processed. Confidential content often ends up in external systems without it being clear where it is stored or how long it will be kept. This can lead to serious data protection violations.
Generative AI systems process user input, analyze it, and use it to improve their models. This creates the risk that sensitive information may indirectly reappear or be used for other purposes. An incident at Samsung that made headlines in the spring of 2023 shows how real this danger is. Employees had copied internal source code and other data into ChatGPT. Because this protected information could become part of future model versions, the company severely restricted access to the AI tool.
In addition, many AI services operate their servers outside the EU. This means that data leaves the European legal area. Companies thus lose the ability to ensure that the GDPR is complied with.
When it comes to the topic of AI and data protection, increased attention is always necessary, as even a single careless action is enough to disclose sensitive information or handle it improperly, ultimately resulting in enormous penalties. Violations of the GDPR are punishable by fines of up to €20 million or 4 percent of global annual turnover.
Compliance violations
In addition to data protection, compliance with other legal requirements also plays a central role. Many industries are subject to strict regulations, such as the financial, healthcare, and legal sectors. Shadow AI can unintentionally undermine these requirements. Lack of documentation, unauthorized data processing, or unclear responsibilities can quickly lead to violations.
Other typical scenarios relate to retention periods, access restrictions, or the traceability of decisions. AI applications that are used without approval generally do not meet specific requirements. The risk of sanctions is very high.
Uncontrolled AI decisions
Artificial intelligence does not provide guaranteed truths. Corresponding models work with probabilities and on the basis of existing training data. Without expert review, results can be flawed, distorted, or simply wrong. Without control by IT or compliance teams, such unreliable results flow directly into important decisions.
A well-known example comes from the US, where two lawyers used an AI system for legal research in 2023 and relied on the sources it provided. These later turned out to be fictitious. The court subsequently imposed a heavy fine. The case clearly shows how risky it is to accept AI outputs without checking them.
Inefficient processes
Ironically, shadow AI often leads to the exact opposite of what users intend: employees hope to save time and achieve better results. However, without coordination, parallel working methods and thus highly inefficient processes arise. Different departments use different AI tools with differing data sets. The results contradict each other, coordination becomes more complicated, and decisions are delayed.
A typical example can be found in marketing, where AI-supported evaluations from external tools can quickly deliver figures that differ from the company's official reporting. This is almost inevitable because they are not integrated into the tech stack and therefore use a less comprehensive information base. Trust is lost, processes become confusing, and efficiency declines.
Lasting damage to reputation
In the long term, shadow AI can also damage a company's reputation. This happens especially when unauthorized AI systems are used by employees without clear quality standards or ethical guidelines. Distorted data, incorrect content, or inappropriate results have a direct impact on external perception.
Publicly known cases also show how sensitive (potential) customers can react. Critical reporting on AI-generated content at Sports Illustrated in the fall of 2023 still attracts derogatory memes, ridicule, and ultimately a significant loss of trust in the journalistic quality of the magazine. Especially in the B2B environment, with products that generally require explanation, or in very sensitive areas such as health or finance, such incidents have a long-lasting effect. Once lost, trust, competence, and industry status are difficult to regain.
How can I rule out shadow AI?
To effectively limit shadow AI, you don't need a rigid set of rules, but rather a well-thought-out combination of strategy, clarity, and communication. It is crucial that artificial intelligence is not banned outright. Instead, companies should create a framework that provides security and at the same time enables meaningful use. A viable AI strategy combines governance, data protection, and information security into a customized overall concept.
What this approach looks like in practice depends heavily on the specific circumstances of your business. Size, industry, data structure, and existing processes play a key role. There is therefore no one-size-fits-all solution. In practice, it has been shown that sound external AI consulting is often useful for realistically assessing risks and deriving appropriate measures. Nevertheless, there are some basic principles that have proven effective almost everywhere.
An important point here: Consciously addressing shadow AI can even open up opportunities. Above all, it highlights where employees currently see a need for (extended) AI support. This knowledge can be used in a targeted manner to anchor artificial intelligence in the company in a structured way.
Creating a culture of collaboration
Open communication between IT, security managers, and specialist departments forms the basis for the responsible use of AI. When teams understand the possibilities and limitations, they are more willing to seek dialogue at an early stage. This allows meaningful use cases to be identified without compromising data protection or security.
It is particularly valuable to actively involve employees. Asking what support is really needed in everyday work provides important insights. This allows risks to be reduced while achieving real productivity gains.
In addition, everyone should be aware of the dangers of unregulated AI use and the tested alternatives available. The following measures are particularly suitable for this purpose:
• Training on the safe use of AI applications
• Digital learning formats with practical examples
• Clear guidelines for the use of generative AI depending on the area of responsibility
Define AI guidelines
Binding rules provide guidance. Companies should clearly define which AI tools are permitted and which may not be used. It is equally important to specify how new applications are tested and approved. Such guidelines typically include:
• An overview of approved and unauthorized AI applications
• Regular training on safe use
• Fixed testing processes for new technologies
A well-designed governance framework keeps pace with the rapid development of AI while ensuring security. It leaves no doubt about how sensitive data should be handled and what responsibilities employees have.
Monitoring tools and audits
Various technical measures are available to support compliance with AI regulations. Security and compliance tools help to identify unauthorized AI use. These include network traffic analysis, protected test environments for new tools, and controlled access mechanisms.
Regular reviews show which applications are actually in use. Such audits help to identify risks at an early stage. At the same time, they open up the opportunity to discover new meaningful areas of application for AI that were previously hidden.
Repeatedly point out dangers
Artificial intelligence is increasingly becoming part of everyday software and apps, with many functions working in the background without being clearly recognizable as AI. This is precisely why there is a (constantly) increased risk that employees will unconsciously use shadow AI.
Regular communication is crucial here. Internal updates, short information formats, or recurring training keep awareness and knowledge of current developments alive. The goal is to promote responsible use in the long term. If the associated risks are continuously made transparent, not least, the general understanding of data protection, compliance, and corporate reputation will grow, which can of course be very beneficial even without any reference to AI.
Conclusion
Shadow AI can quickly become a serious risk for companies. Serious data protection violations, legal consequences, incorrect decisions, and damage to reputation are real dangers. At the same time, however, a structured approach to the issue also offers opportunities that should not be overlooked. Audits reveal new applications, employees provide valuable input, and cooperation between departments improves noticeably. Awareness of security and responsibility grows and remains present. Professional AI consulting helps you develop appropriate guidelines and establish artificial intelligence securely in your company in the long term.
FAQ
Why does shadow AI arise?
AI tools are easily accessible and widely used today. Studies show high usage in everyday life. As a result, AI used privately is often simply used at work without considering the potential risks.
Does shadow AI offer opportunities for companies?
Yes. With the right strategy and appropriate technical means of detection, it shows where employees see a real need for (additional) AI support. This knowledge can be used to deploy AI in a targeted, secure, and ultimately productivity-enhancing manner.
How can shadow AI be detected?
This can be done with the help of specific technologies as well as on a human or strategic level. Technically, monitoring tools and regular audits help. Strategically, an open conversation is often sufficient. Many employees do not see external AI applications as problematic. Instead of sanctions, targeted education is the more sensible (and promising) approach.
Posted by:
Jens is the co-founder of bakedwith, a boutique consultancy firm specialising in smart automation and AI. He helps medium-sized companies and corporations to optimise processes, reduce manual work and achieve growth through efficient workflows. Previously, he was Growth Lead at OMR and worked as a freelancer, helping numerous companies optimise their CRM and automation systems. Jens is passionate about combining growth and efficiency to enable teams to focus on what matters most.
