In this session, learn:
- How machine learning and AI are automating advanced threat detection
- Why 100% network visibility allows you to preempt emerging situations, in real time, across both IT and OT environments
- How smart prioritization and visualization of threats allows for better resource allocation and lower risk
- Real-world examples of detected OT threats, from non-malicious insiders to sophisticated cyber-attackers
Cyber security is an almost impossible problem to solve. This is particularly true in industrial environments, which have long faced some of the most advanced attackers, from espionage in the energy sector to state-sponsored threats. As IT and OT environments converge and the growth of industrial IoT continues to broaden attack surfaces, perimeter defenses and airgapping simply aren’t enough anymore.
Such highly bespoke environments mean that security teams have long been resigned to lengthy, costly deployments too inefficient to keep up with system updates and expansion. Old, even proprietary, standards, unpatched operating systems, and a reliance on rules-based security approaches abound, and the complexity of these ICS/SCADA networks means they are all too often neglected by today’s security providers.
While total prevention of compromise is untenable, utilizing automated self-learning technologies to detect and autonomously respond to emerging threats within a network is an achievable cyber security goal, irrespective of whether the suspicious behavior originated on the corporate network or ICS. Some of the world’s leading energy and manufacturing companies are relying on immune system technology to identify the earliest indicators of cyber-attacks across their network environments and mitigate threats before damage is done.