Systemic AI risk analysis primarily falls within the domains of Artificial Intelligence, cybercrime and cybersecurity, and digital transformation. The term describes an approach where risks arising from the use of artificial intelligence are considered holistically and in context. This does not just refer to individual errors or vulnerabilities, but to the interplay of many factors affecting the entire system.<\/p>\n
Imagine a company uses AI to analyse customer data more quickly. A systematic AI risk analysis would now ask not only, \u201eCan the algorithm be wrong?\u201c but also, \u201eWhat happens if several departments become dependent on these results?\u201c or \u201eWhat are the consequences if hackers penetrate the system and manipulate the AI?\u201c<\/p>\n
This approach helps to identify early on how vulnerabilities in one area can spill over into other areas. This allows targeted measures to be taken before actual damage or failures occur. For decision-makers, this means greater security when deploying AI \u2013 especially at a time when digital attacks and complex system interdependencies are on the rise.<\/p>","protected":false},"excerpt":{"rendered":"