Shadow AI in Companies: Understanding & Solving Risks

Shadow AI has long been a reality: Employees use AI tools such as ChatGPT, Midjourney or Copilot in everyday working life — but often without approval, without governance and without awareness of the consequences. For companies, this results in a mix of efficiency gains, security risks and legal issues that should not be ignored.

‍

‍

Shadow AI: What is behind the term?

Shadow AI is the unapproved use of AI tools by employees — i.e. AI applications that are used without the knowledge or consent of IT, data protection or management. These include generative AI chatbots, image and video AI, translation tools or code assistants that are used via private accounts or devices.

The term is based on “shadow IT”: systems and tools that are introduced past official processes. The difference: Shadow AI directly affects sensitive content — texts, customer data, drafts, source code — and can therefore directly jeopardize data protection, compliance and know-how protection.

Important: Shadow AI is not created out of bad faith, but usually out of pragmatism. Employees want to work more efficiently and solve problems faster and therefore use tools that they know from a private context — often because official solutions are missing or are perceived as too slow.

‍

‍

How big the problem really is

Numerous studies show that shadow AI is already widely used in everyday business — far beyond individual cases:

• An international KPMG study reports that over half of employees uses AI tools without official approval.
• According to Handelsblatt survey, around 70% of employees in German companies Use AI tools yourself at work — often via private accounts.
• A Bitkom survey shows that the proportion of employees who specifically use AI without the employer's knowledge has doubled within a year.

At the same time, many companies state not a complete overview to know the actual use of AI in the house. Studies on AI ROI and Flexera surveys confirm that IT managers usually underestimate the spread of shadow AI — despite a high level of trust in employees.

The key message: Shadow AI is not a marginal phenomenon, but a structural issue. If you ignore it, you not only lose control, but also the opportunity to channel the available energy in the company in an orderly manner.

‍

Typical deployment scenarios & examples of shadow AI

Shadow AI appears wherever employees feel gaps in processes or tools. Common scenarios include:

• Write or revise texts: emails, offers, presentations, social posts with generative text models.
• Create images, slides or layouts: presentation slides, mood boards, campaign drafts with image AI.
• Analyze data: Upload Excel files to AI tools to get quick summaries or evaluations.
• Programming & IT: Use of code assistants and external repositories for error analysis.

Examples of shadow AI in everyday life: An employee loads a customer presentation into a public AI tool to optimize structure and texts. A developer copies code snippets into an external chatbot to identify bugs. Or HR uses AI to pre-select CVs, although this is considered a high-risk use case under the EU AI Act and particularly strict requirements must be met.

Each of these examples is understandable on its own — but the overall result is a shadow AI landscape that is neither documented nor controlled. This is where the risks start.

‍

‍

Shadow AI in companies: causes and drivers

Shadow AI is a symptom, not an accident. Typical drivers include:

First: Inappropriate or inadequate official AI tools. Many companies introduce a specific solution “from above” without really understanding the requirements of the specialist areas. Employees find the tools complicated, too limited, or not available when they are needed — and switch to familiar, external offers.

Second: Slow decision-making and approval processes. The introduction of new tools is often linked to traditional IT processes: evaluation, procurement, approval, training. At the same time, teams are under severe time and performance pressure. When official structures are too slow, shadow AI emerges as a pragmatic abbreviation.

Thirdly: Lack of information and clear rules. Many employees simply do not know what is allowed — and what risks are associated with shadow AI. If there is no comprehensible AI guideline, personal assessments fill the gap. This is risky from a company perspective, but understandable from an employee's point of view.

‍

‍

Legal, security and business risks

‍

1) Data protection/compliance

The biggest risk of shadow AI in companies lies in data protection and compliance. If customer data, internal documents or personal information are loaded into external AI tools without approval, there is a risk of violations of the GDPR, confidentiality agreements or industry rules.

High-risk use cases within the meaning of the EU AI Act, such as AI-based candidate pre-selection or decisions with a significant impact on those affected, are particularly critical. If employees use shadow AI here without the company knowing about it, this results in a massive blind flight with possible liability consequences.

‍

2) IT security

Shadow AI can also create new attack surfaces: unsecured web tools, opaque data flows, vulnerable browser plug-ins, or insecure API connections. If these applications do not appear in official monitoring, patches, logging, and emergency processes are missing.

In addition, employees can unintentionally “learn” sensitive internal information into external models — for example by repeatedly uploading similar data. This makes it difficult to control know-how and intellectual property.

‍

3) Quality and reputation risks

Generative AI can hallucinate — i.e. provide false, distorted, or outdated information. If shadow AI content ends up unchecked in presentations, customer documents or contracts, technical errors and reputation damage are inevitable.

There is also a risk that the tonality of AI does not match the brand: texts suddenly appear generic, impersonal or contradict existing communication guidelines. Especially when it comes to sensitive topics — such as compliance, HR or medical content — trust can be permanently damaged.

‍

4) Costs and chaos in the tool stack

Shadow AI often provides parallel structures: licenses paid twice, unclear responsibilities and a lack of overview of the actual use of tools. This makes support, training, and governance difficult — and makes it difficult to realistically assess the ROI of AI investments.

‍

‍

Shadow AI in companies: Why bans alone won't help

Many companies are reflexively responding to shadow AI with bans — in the hope of being able to “eliminate” risks. Experience shows, however, that this barely works in practice. Employees continue to use AI, just in a more hidden way.

The reason is simple: The operating pressure is high. AI tools save time, reduce routine work and deliver good designs quickly. When official options are missing, teams find their own ways. Bans without alternatives therefore primarily lead to loss of trust, more shadow AI and a missed learning curve.

Instead, you need one Change of rules: Away from the question “How do we keep AI out? “to “How do we enable AI safely, responsibly and productively? ”. Shadow AI then becomes an invitation to address the issue in a structured way.

‍

Shadow AI in companies: 5 steps to an open AI culture

‍

1. Make shadow AI visible — without blaming

The first step in dealing with shadow AI is transparency. It makes sense to have an open discussion with teams: Which AI tools are already being used? For which tasks? What experiences are there — positive and negative? It is crucial to communicate clearly: It is not about “catching” someone, but about understanding reality.

‍

2. Structure shadow AI with a simple “AI traffic light”

Instead of writing long guidelines, a AI traffic light Create orientation quickly:

• green: Approved tools (e.g. internal AI platform, certified translators)
• yellow: Allowed with clear restrictions (e.g. only synthetic or anonymized data)
• red: Prohibited, such as uploading sensitive customer data to free public tools

This traffic light should be clearly documented, easy to find and regularly updated — ideally with examples from your own daily work.

‍

3. Build up knowledge about shadow AI & safe use of AI

Technical requirements alone are not enough. Employees need concrete knowledge:

• What are the risks of shadow AI in our context?
• Which data is never allowed into external systems?
• How do I critically check AI results?

Practical training, internal AI guides and “prompt playbooks” create security and help transform shadow AI into responsible use.

‍

4. Replace shadow AI with attractive official AI solutions

If official AI solutions are slower, less practical, or worse than shadow AI, they won't be used. Companies should therefore consciously invest in good, user-friendly AI platforms, such as enterprise AI with single sign-on, tested models and clear data rooms.

The goal: Employees experience that “the official path” is better than the dark path — because it is faster, safer and more practical. This reduces the incentive to continue using shadow AI in companies.

‍

5. Establish AI governance for shadow AI

In the long term, every company needs a clear AI governance framework: Roles, responsibilities, approval processes, monitoring and escalation paths. Shadow AI should be addressed explicitly — including reporting channels, how new tools can be proposed and evaluated together.

‍

‍

Roles, Responsibilities & Governance

Shadow AI is not just an IT issue. Effective use requires the interplay of several functions:

• IT & information security: Technical approval, monitoring, network protection, tool selection
• Data protection & law: Assessment of data flows, DPA, EU AI Act, GDPR compliance
• HR & works council: Training, employee participation, regulations in the work context
• Disciplines: Definition of useful use cases, feedback on tools and processes

Clear AI governance around shadow AI should include:

• How new AI tools are proposed and evaluated
• Who decides on approvals and how they are documented
• How violations are handled (focus on learning rather than punishment)
• How regularly is it checked whether guidelines are still practical

This is how shadow AI goes from uncontrolled risk to manageable change — and part of a conscious AI strategy.

‍

‍

Shadow AI in companies: FAQ

‍

Is shadow AI an issue in every company?

In almost all knowledge work environments today, it is realistic to assume that shadow AI is at least partially available — even if there are no official AI projects. The easy availability of AI tools and the high usage pressure make complete “abstinence from AI” rare in practice.

‍

Does shadow AI have to be completely banned?

A blanket ban on AI tools is hardly practicable and usually results in shadow AI becoming even more invisible. An approach that clearly addresses risks, provides safe alternatives and involves employees is more sensible. The goal is not “zero use,” but controlled, responsible use of AI in the company.

‍

How can I tell if shadow AI is a problem for us?

References to shadow AI include texts that are suddenly “too perfect”, extremely fast results, eye-catching AI image styles, or frequent mentions of certain tools without official introduction. Technical measures such as network analysis and tool scans can complement the picture — but they are not replaced by open discussions with the teams.

‍

What role does the EU AI Act play in shadow AI?

The EU AI Act requires transparency, documentation, and risk management for certain AI applications — particularly in high-risk areas such as HR, lending, or critical infrastructure. Shadow AI undermines these requirements because usage and systems are not documented. Companies should therefore explicitly integrate shadow AI into their AI-Act implementation.

‍

‍

Shadow AI in companies: Conclusion and next steps

Shadow AI shows where reality in the company is ahead of the official AI strategy. Employees are already using AI — often pragmatically, sometimes risky, almost always with the aim of doing their jobs better. Looking away is not an option: If you ignore shadow AI, you risk data protection problems, security breaches, and loss of trust.

At the same time, shadow AI is an opportunity: It makes visible where AI really helps in everyday life, which tools are needed and which processes are too slow. Companies that take up these signals, establish clear AI governance and build an open AI culture win twice — more security and more productivity.

The KI Company supports organizations precisely at this point: from analyzing existing shadow AI patterns to developing pragmatic AI guidelines to introducing secure corporate AI solutions and training for teams. If you no longer want to use shadow AI in your company as a risk but as a starting point for a structured introduction of AI, you can always contact us without obligation.