Cybersecurity threats often originate from errors committed by humans when performing operational tasks. Anomalies in network activity can hint at imminent or ongoing cyberattacks. AI & ML are particularly effective at countering both, reducing errors and faults in daily tasks and detecting anomalies and irregularities.

On the other hand, ML techniques will make sophisticated cyberattacks easier and allow for faster, better-targeted and more destructive attacks. The impact of AI on cybersecurity will likely expand the threat landscape, introduce new threats and alter the typical characteristics of threats.

Finally, as AI becomes woven into commercial and governmental functions, the consequences of the technology’s fragility are momentous. AI systems will also become increasingly subject to manipulation themselves. Research is underway to develop systems that are more resilient.

In this track, we explore the role of AI for cybersecurity – its blessing and its curse – and how the private sector, government and academia should collaborate to reduce the threat landscape of AI systems as well as to isolate them with safeguard mechanisms that make it easy to shut down if things start to go wrong.