The COVID-19 pandemic is an example of a temporary situation when critical infrastructure (CI) operators had to operate with continuously changing conditions. The role of cyber infrastructure during pandemics, for example for the remote work or access to critical systems, has also changed.

The feature extraction capabilities of Machine Learning (ML) models have led to their wide adoption in a large variety of sectors: from anomaly detection for machinery, to user clustering and behavioral prediction, market trends predictions, or the analysis of text, sound, and image data.

In the world of finance, where every decision can have significant ramifications, the possibility of predicting market movements is invaluable. Traditionally, analysts have relied on a combination of data analysis, market trends, and expert insights to make informed predictions.

Due to its multivariate and multipurpose use and reuse, data’s worth is dramatically increasing, leading to an era characterized by the generation of data marketplaces towards accessing, selling, sharing, and trading data and data assets.

The fact than more and more Cyber-Physical or IoT systems are integrated in vehicle fleets, coupled with the growing connectivity of these devices, poses a higher risk of cyber attacks, due to the increased attack surface.

With the increased digitalization and adoption of cost-effective off-the-shelf components and cyber connectivity, Critical Infrastructures (CI) operators have benefited in many ways, but the attack surface has also become larger.