Operating AI applications safely: Checks after the pilot
By Nico Freitag, Geschäftsführer, Cybersecurity und Governance
An AI pilot only becomes a resilient operation through clear responsibility, controlled data paths, measurable quality limits, logged changes, human intervention options and a practiced incident path.
What changes during the transition to business?
In the pilot, the number of users, data and consequences can be deliberately limited. Dependence and the pace of change increase in the company. Therefore, document the purpose, user roles, providers, models, data sources and permitted decisions as a current system inventory. Every significant change needs a professional and technical assessment before it quietly becomes the new normal.
Which controls must be measurable?
Set quality limits, permitted errors, human checking and termination criteria for typical and critical cases. Monitor not only technical availability, but also technically incorrect output, unusual usage, costs and feedback from affected users. The NIST Profile for Generative AI complements the AI RMF with specific risk considerations for such systems.
How does the company remain able to act?
Version model, prompt, knowledge base and key configuration. Limit access and secrets, log relevant changes and have a fallback path in place. An incident process must clarify who blocks expenses, stops data paths, contacts providers and informs users. Legal classification, data protection and information security are considered together.
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Frequently asked questions
- Is normal infrastructure monitoring enough for an AI application?
- No. Additionally, professional quality, unexpected expenses, data issues, usage, costs, and human corrections should be monitored.
- Does every model change have to be released again?
- The scope of the test depends on the risk and impact. However, changes must be understandable and must not tacitly change established quality or protection limits.