Today, AI makes it seem easier than ever to generate highly complex IT solutions with just a few prompts, create comparison tables of different solutions, provide evaluations, or request recommendations. This quickly creates the impression that anyone—without prior knowledge and based solely on AI responses—can make well-founded decisions.

 

This is exactly where a completely new risk emerges for companies: When anyone is suddenly considered an expert in a field simply because they evaluate AI-suggested solutions without taking the company's actual infrastructure and real-world conditions into account, it can rapidly turn into a serious security risk.

 

AI-supported evaluations are often based on assumptions. Solutions can only be assessed fully and realistically when viewed in their overall context. A solution might perfectly meet an organization's needs when considered in isolation.

 

In practice, however, it is just one piece of the entire IT infrastructure—and that is precisely where it often fails: due to incompatibility with the existing architecture, excessive administrative costs, impracticality for end users, or security weaknesses.

 

AI frequently delivers fast answers that completely ignore the complexity of the overall IT landscape.

 

A sound evaluation requires that all parameters and boundary conditions of the operational infrastructure be laid out in detail. What matters most is which problem needs to be solved and which threats are realistically relevant to the organization.

 

The core principles of IT security—availability, confidentiality, and integrity—should always serve as the foundation for evaluation questions:

 

How does the solution ensure service availability, and how is data or service recovery guaranteed in case of loss? What happens in the event of device loss or security incidents, and how are solutions protected?
Are state-of-the-art antivirus and logging tools implemented? How is access control and auditability handled?

 

Another key aspect is administrative overhead: Ideally, a solution should require as little ongoing manual administration as possible after initial setup.

 

Exit capability should be a central evaluation criterion: How dependent does the solution make the company on the vendor or platform? How easily can the company retrieve its data? Admittedly, this question rarely arises for most decision-makers as long as everything runs smoothly.

 

But what happens when prices rise, contracts expire, or strategic changes demand new solutions? A resilient solution is always characterized by the company retaining full control and sovereignty over its data and systems.