Artificial intelligence (AI) has become so disruptive that dealing with it has become a critical issue for companies. Some organizations aim to take on a pioneering role, while others are still resisting the new reality.
The fact that the latter is definitely not the best approach is clearly demonstrated by the high prevalence of shadow AI in many companies. This type of AI usage, which bypasses internal IT, may initially appear practical and helpful, but it often carries unpredictable risks.
This article examines these risks in detail and explains what an effective approach to shadow AI can look like.
What Is Shadow AI?
The umbrella term “shadow AI” refers to artificial intelligence applications that have not been approved by a company’s IT department. The corresponding tools and platforms are used without authorization, with employees hoping to gain advantages such as time savings, reduced effort, and higher productivity. In short: these are privately used applications that are not part of the company’s official tool stack.
This does not become a serious problem until data protection violations, poor decisions, and hallucinations occur, which can become very costly for companies. In addition to sensitive security vulnerabilities and data breaches, reputational damage and financial penalties may also result.
Examples
Uncontrolled AI usage is common in companies and appears in different forms. These include:
- Generative AI, for example for text creation and image generation
- AI analysis tools for business reports
- Coding assistants
- Automated email response generation
- Service assistants
- Risk assessment tools
In a specific example, an employee may need to prepare a business report that requires the inclusion of large amounts of complex data. Because time is running short and IT apparently has not provided a suitable AI tool, the employee quickly decides to use an external tool independently.
For the tool to analyze, interpret, and summarize the data, the employee must share a large amount of sensitive company information. This creates data protection violations and security vulnerabilities because it is unclear where the data is going and who now has access to it. AI tools also use inputs as training data, meaning internal company information could directly end up in the hands of competitors.
Shadow AI vs. Shadow IT
Shadow IT has long been a known term and continues to concern IT departments. It refers to employees using software, hardware, and other technologies that have not been approved and are therefore used without supervision and usually without the IT department’s knowledge.
Shadow AI, on the other hand, is relatively new and is becoming increasingly relevant with the growing use of generative AI in companies. As the name suggests, it refers to employees using artificial intelligence without authorization. Therefore, shadow IT is the broader term, while shadow AI is the more specific one.
Both represent a serious problem because the risks are unpredictable and beyond the control of the IT department.
Problems Caused by Shadow AI in ITSM
When employees frequently use shadow AI, this also becomes a serious problem for IT service management (ITSM). The main reason employees bypass standard processes is that AI tools provide quickly usable answers, whereas traditional ITSM processes rely on triage, prioritization, and standardized workflows.
From a security perspective, shadow AI creates major problems in ITSM because all incidents and issues that bypass the service desk remain invisible and effectively nonexistent as data.
- Recurring errors are not visible.
- Root-cause analyses become significantly more difficult.
- It becomes harder to identify how many incidents occur.
- It is nearly impossible to determine which incidents concern employees the most.
- Security Operations Centers (SOCs) face increased analysis workloads.
From an ITSM perspective, shadow AI therefore blocks insights and progress. It also prevents the development of standardized solutions for incidents, which can lead to superficial fixes and follow-up errors.
Since shadow AI cannot simply be ignored, the key question is how companies can contain it, maintain security, and provide adequate alternatives within their official technology stack.
OTRS offers flexible AI applications that are ideally suited to introducing controlled and beneficial AI usage within ITSM.
The Risks of Shadow AI
It is difficult to defend against something that is invisible. The overarching risk of shadow AI is therefore that it operates outside of control and beyond existing regulations and policies.
One could argue that shadow AI demonstrates employee ingenuity, has important experimental value, and increases productivity. That is partly true, but it comes at a price—sometimes a very high one—that is rarely proportional to the benefits achieved.
The following risk factors illustrate the dangers posed by shadow AI:
Data Protection Violations and Security Vulnerabilities
Because AI usage in this context is unsupervised, employees may share sensitive data, increasing legal and compliance risks and potentially leading to significant consequences in the event of a data breach. In many cases, data breaches represent a greater risk to companies than external threats.
Employees often disclose data carelessly because they are not fully aware of the associated risks and perceive the advantages to outweigh them. This creates security violations that increase the organization’s vulnerability and can lead to successful attacks.
Compliance Violations
When employees transmit sensitive data to third-party providers while using shadow AI, this violates regulations such as the General Data Protection Regulation (GDPR), internal agreements, and confidentiality obligations. In serious cases, companies may face substantial penalties for such violations.
Hallucinations
It is no secret that AI tools can hallucinate. Due to insufficient data, incorrect interpretation patterns, and the need to produce convincing answers even with limited information, generative AI in particular is vulnerable to this issue.
When employees use private AI applications for work purposes, the responsible IT department cannot evaluate them according to applicable quality standards or provide appropriate training on handling hallucinations. As a result, outputs may be fundamentally incorrect. This becomes especially serious when AI has access to correct data but presents it in the wrong context.
Manipulated Data
Data can also be manipulated. Company software generally includes strict security protocols and proper monitoring. Shadow AI, however, often involves applications that were not specifically developed for enterprise use and therefore do not meet the same security, control, and update standards. This increases the risk that attackers can gain access more easily and manipulate data for various purposes.
Unreliable Quality
The quality of the results generated by shadow AI applications may be high, but it is difficult to assess. In reality, quality is often lacking because shadow AI is frequently used hastily and without proper review. Whether employees can reliably achieve high-quality results with these tools is therefore questionable.
Damage to Reputation
A strong reputation is one of the most valuable assets a company can have. Companies do not want to risk it carelessly. Yet that is exactly what happens when shadow AI causes data protection violations, compliance breaches, hallucinations, questionable outputs, or even successful cyberattacks, and these incidents become known to customers or the public. Companies can quickly develop a poor reputation and may struggle to regain lost trust.
The Main Causes of Shadow AI
Generative AI applications have gained overwhelming acceptance in recent years and are used in virtually all organizations. At the same time, many companies do not provide comprehensive AI applications that specifically benefit their employees.
Where official AI applications do exist, employees certainly use them. However, especially when facing special requirements, high expectations, and limited time, employees often switch to tools they believe are best suited for the task.
The following are the main reasons and motivations why employees turn to shadow AI:
1. Missing or Inadequate Company Offerings
Some companies have fallen significantly behind in AI adoption. In many other organizations, there are still gaps in the tailored use of AI solutions for specific needs. In both cases, many employees feel left on their own and independently turn to AI tools.
2. Greater Productivity and Time Savings
Employees must invest significant time and effort into certain tasks, especially under heavy workloads, which prevents them from focusing on more value-creating and creative work. To complete everything in the available time, it seems logical to use unauthorized AI applications, especially when suitable official AI tools are not quickly and easily accessible.
3. Limited Understanding of AI Risks
Many employees are unaware of the risks posed by shadow AI. For them, the benefits outweigh the risks, and they assume nothing serious will happen. In fact, the use of shadow AI on a larger scale often remains without consequences.
However, some cases result in severe consequences that reveal the true extent of the risks. Even in seemingly harmless situations, companies lose control over the flow of data and information, not to mention the questionable quality of AI-generated results.
4. Too Much Bureaucracy and Restriction
This problem is frequently observed in companies. In principle, employees want to follow the applicable rules and procedures. However, too many regulations, lengthy approvals, and exhausting processes make it difficult for them to follow the official path.
Restrictions and limitations on certain AI applications also encourage employees to use them independently without authorization. The vicious cycle is as follows: too many regulations lead to more shadow AI, and more shadow AI leads to stricter regulations—which are equally ineffective for companies.
5. Personal Benefits and Overload
Many employees strive to present themselves as particularly innovative, progressive, and productive through the use of AI. The benefits go in two directions: on the one hand, they hope to improve their standing with their employer; on the other hand, the time savings mean they personally have to work less.
Many employees also resort to shadow AI when they cannot otherwise manage their workload satisfactorily within regular working hours. In this case, shadow AI becomes a response to overload.
Best Practices for Dealing With Shadow AI
For many companies, the key question is how to best handle the use of shadow AI. First of all, it cannot simply be ignored or dismissed. A large proportion of companies are affected by shadow AI usage, making the right approach essential.
The following practices and approaches can help:
1. Encourage Collaboration
Employees often resort to shadow AI because productive discussions about AI have not taken place and, as a result, they do not have access to the tools they need. Clear and open communication between IT and security teams and the workforce can help identify suitable AI tools while also ensuring compliance with data protection requirements.
2. Allow Flexibility in Governance
Governance should provide guidance without unnecessarily slowing employees down. The challenge is to support AI adoption while also providing clear and practical guidelines. Fast approval processes and uncomplicated procedures are essential for effectively preventing shadow AI usage.
3. Regularly Inform Employees About Risks
Companies should repeatedly inform employees about the dangers of shadow AI. Particularly from an IT and security perspective, organizations often overestimate how well employees understand these risks and engage with them. Since AI applications continue to evolve rapidly, companies should regularly provide updates on risks and current developments.
4. Survey Employees About AI Tools
Traditionally, the IT department determines the tool stack for employees. One major reason for the use of shadow AI is that the official stack does not suit everyone and does not always provide quick solutions for all areas. Surveys and consultations are therefore a meaningful way to obtain direct feedback and provide the right AI tools.
In general, companies should strive to find a healthy balance between employee autonomy and trust on the one hand and active measures against shadow AI on the other. Strict monitoring is usually not effective because it promotes mistrust and does not address the problems employees are trying to solve independently through shadow AI.
Instead, the focus should be on an approach that minimizes the need for shadow AI while also increasing awareness of risks. This works through the right official AI tools and open dialogue between IT and security teams and employees.
Conclusion: Companies Must Take Shadow AI Seriously
Shadow AI – artificial intelligence applications used outside a company’s official technology stack – has become an important issue that organizations should closely monitor. There are many different reasons why employees turn to it.
In most cases, this is a mixture of perceived quick benefits and low awareness of risks. The risks range from a lack of transparency and therefore limited control by the IT department to quality deficiencies, security vulnerabilities, and substantial penalties.
Companies are now required to develop a balanced approach to shadow AI that limits its use without unnecessarily restricting employees’ ability to act.
The following measures are especially helpful:
- Providing suitable AI applications
- Creating greater awareness of risks
- Moderate control without monitoring employees
OTRS provides flexible AI applications that can significantly reduce workloads, especially at the service desk.