Expert article: What is shadow AI and what risks does it pose?

How does shadow AI arise?

Shadow AI arises from the use of AI tools that are not approved or visible to IT, data protection or compliance teams. The reason is simple: AI is fast, accessible, and more closely aligned with everyday work than many official systems. Anyone wanting to write a more professional email, follow up on a meeting or create a first draft of a text will take the shortest route. If there is no accepted alternative, or if the rules for using one are too vague, shadow AI is the obvious choice.

What risks does shadow AI pose to your company?

Damage caused by shadow AI does not arise directly from the unofficial use of AI tools. However, this usage method often results in important content, sensitive data, and internal company information being leaked to the outside world.

It is important to distinguish between shadow AI and shadow IT. The latter requires technical expertise and has higher hurdles, as well as established detection strategies. In contrast, shadow AI happens in seconds within the browser, remains invisible and, in the worst case, can result in company data falling into the wrong hands.

This has two immediate consequences: data leakage and blind spots. Employees may copy customer messages, upload logs, or provide the AI with contracts. Even short excerpts can reveal a lot of context and may contain confidential information. At the same time, there is a lack of logs, central approvals and feedback. Risks only become apparent when it is too late to act.

How can shadow AI be managed securely?

Rather than preventing the use of AI, companies must enable it safely. This requires clear rules, effective alternatives and technical guidelines that support, rather than hinder, daily work. The risk can be reduced step by step.

1.Firstly, clear responsibilities and simple rules are needed so that everyone knows what
   is permitted.

2.Next, an approved AI system that is easily accessible and under the company's control
   should be implemented to make everyday life easier.

3.Next, technical guidelines such as DLP (Data Loss Prevention) and upload warnings are
   introduced, along with practical training. DLP (Data Loss Prevention) is a security
   solution that prevents sensitive data from being shared, lost or misused without
   authorisation.

4.Finally, there is a clear emergency plan for serious incidents. This improves
   transparency and encourages active employee engagement, thereby enhancing
   everyday work. Productivity increases.

How can you tackle shadow AI in a structured way? With a 7-day plan!

This 7-day plan enables companies to track the reduction of shadow AI.

Day 1: Taking stock. Clarify what has been done with AI so far, what it is needed for and what ideas for its use already exist, in a structured manner. Highlight any areas involving sensitive data. This ensures that the starting point is clearly documented in technical and content terms.

Day 2: Understanding and communicating risks. Identify the key risks and explain them in clear, everyday language. Communicate the dangers openly within the company and describe the potential consequences of uncontrolled use. This will foster a shared understanding of the need for action and the desired outcomes.

Day 3: Find and provide a secure alternative. Select an AI system that can be operated in a controlled manner. Clearly state where the data will be processed. For example, it could be processed in an external data centre or entirely within the company. It is crucial that the alternative is convincing in everyday use and that employees voluntarily choose to use it.

Day 4: Set out simple rules. Define clear and concise guidelines for everyday life that everyone can understand and apply. Make sure the guidelines are short enough to be read and precise enough to provide guidance.

Day 5: Deliver training and an AI workshop. Use real-life examples to demonstrate what is and isn't permitted and how to work safely. The focus will be on practical situations, making the rules immediately tangible in everyday work.

Day 6: Activate guardrails for AI systems. Implement warnings, upload checks or DLP rules where data leakage occurs. These guardrails are designed to provide early warning of risky inputs, not to block them.

Day 7: Gather feedback and make improvements. Ask what is working well and what is holding things back. Use this feedback to improve the rules and offering consistently. This will increase acceptance and ensure the process remains adaptable.

What does shadow AI mean for your company?

Shadow AI arises when a high pace of change is coupled with a lack of alternatives. The risk increases because data leaves the company unintentionally, and there is no overview. Enabling secure use creates transparency, protects data and increases productivity. The task now is to establish the appropriate structures, enable secure usage, and continually enhance utilisation. This will enable you to keep pace with rapid technological developments.

Author:

Stefan Fenn is a mathematician and computer scientist as well as CEO of Smart Labs AI. With many years of experience in developing complex software solutions for banks and insurance companies, he combines in-depth mathematical expertise with practical software architecture.

 At Smart Labs AI, he is primarily responsible for testing and hardening AI systems and ensuring that they can be reliably, securely and robustly integrated into existing business processes.