AI Cyber-Defence and Decentralized Reliability for Industrial Data

The integration of AI has unlocked a realm of transformative capabilities such as automatic visual inspection, predictive maintenance, and optimizing production lines. However, it has also introduced new security challenges, as malicious actors exploit the interactions between AI and legacy ICT systems. Adversarial Machine Learning (AML) has emerged as a significant concern in critical AI applications, involving techniques that manipulate data to alter AI algorithm behaviour while it may remain unnoticed by humans. 

This use case focusses on the implementation and testing of the cyber-security components developed in the topic of Security and Data Governance for AI Systems in Manufacturing. The goal of this use case is to ensure safe systems, protected against various cyber-attacks, meaning that the implemented AI systems are protected and safe to use within a manufacturing environment.