Abnormal production processes and cybersecurity

Anomaly detection with artificial Intelligence refers to the automatic detection of deviations or anomalies in a data set. Different types of AI technologies such as machine learning, deep learning or predictive analytics can be used for this purpose.

One use case, for example, is anomaly detection in industrial production (for example, in the dairy or meat industry). Here, AI can be used to detect deviations in the production process at an early stage and take measures to avoid downtime or quality problems. By analyzing sensor or production data, AI can detect and respond to abnormal patterns in real time.

Another use case is anomaly detection in the field of cybersecurity (for example, in the gambling industry). Here, AI can be used to detect potential cyberattacks or threats before they cause harm. By analyzing network traffic or log files, the AI can detect unusual activity as well as patterns and issue alerts.

Another use case is anomaly detection of master data in SAP® systems using AI. This can help to identify and correct problems related to master data at an early stage. Master data forms the basis for many business processes in SAP® systems and is therefore of crucial importance for the continuity of a company.

AI-based anomaly detection of master data is based on the analysis of data over a period of time. Here, AI technology can be used to identify anomalies or outliers in the data, which in turn can indicate potential problems. Examples of anomalies may include unusual patterns in the creation or modification of master data, as well as incorrect or incomplete master data or delays in the processing of master data.