In highly dynamic data center environments, this is precisely where a central area of tension arises between technical complexity, incomplete transparency, and operational responsibility.
Modern data centers are highly virtualized, software-defined, and closely networked. In security-critical situations—such as serious system anomalies, malfunctions, or unclear threat scenarios—it may be necessary to take individual physical systems offline in a targeted and controlled manner. Such kill switch decisions serve to protect infrastructure, data, and connected services, but they also carry considerable risks.
These interventions are particularly challenging in virtualized environments. Virtual machines and services are not uniquely tied to individual physical servers, while documentation, DCIM systems, and logical infrastructure models may deviate from the real situation due to manual changes or dynamic reconfigurations. Under time pressure and with incomplete information, the risk of wrong decisions increases – with potentially serious economic, operational, and liability consequences.
Against this backdrop, aixit conducted an internal scientific innovation project to investigate how high-resolution energy measurement data from physical servers can be used as a reliable physical reference. The aim was to use the energy signatures of individual systems as a kind of “security anchor” to compare logical infrastructure models with actual system behavior and to detect misassignments between documented and real infrastructure at an early stage.
The research approach developed combines continuous energy measurements at the server and rack level with analytical modeling and realistic test scenarios. In contrast to classic security and monitoring approaches, which primarily rely on software-based status information and anomaly detection, this creates an additional layer of security on a physical basis. This increases the transparency of complex infrastructures and significantly reduces the risks associated with security-critical interventions.
The results of the project show that energy reference data can make a valuable contribution to securing operational decisions – especially where time pressure, system complexity, and uncertainty converge. In this area, too, aixit underscores its commitment to driving innovation in a practical, data-driven manner with a clear focus on real operational and security requirements.
