Systemic AI risk analysis is primarily used in the areas of artificial intelligence, cybercrime and cybersecurity as well as digital transformation. The term describes an approach in which risks arising from the use of artificial intelligence are considered holistically and in context. It is not just about individual errors or vulnerabilities, but about the interaction of many factors that affect the entire system.
Imagine a company uses AI to analyse customer data more quickly. A systemic AI risk analysis now not only asks: "Can the algorithm be wrong?", but also: "What happens if several departments become dependent on these results?" or "What are the consequences if hackers penetrate the system and manipulate the AI?"
This approach helps to recognise at an early stage how weak points in one area can spill over to other areas. In this way, targeted measures can be taken before actual damage or failures occur. For decision-makers, this means greater security when using AI - especially at a time when digital attacks and complex system interdependencies are on the rise.
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