As IT and Operational Technology (OT) environments continue to converge, managers of ICS have been faced with the challenge of protecting these crucial systems and data, in spite of inherent security weaknesses and the continual risk of insider threat. In many industrial processes, reliability of an ICS has a direct and immediate impact on the safety of human lives. Existing, legacy approaches have proven inadequate on their own, especially against insiders who, by definition, have authorized access.
There is an urgent need for a new approach to combat the next generation of cyber-threats, across both OT and IT environments. While total prevention of compromise is untenable, utilizing automated self-learning technologies to detect and 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 using these technologies to detect early indicators of cyber-attacks or vulnerabilities across IT and OT environments, without reliance on pre-identified threat feeds, rules, or signatures. These technologies represent an innovative and fundamental step-change in automated cyber-defense.
In this session, learn:
- How new machine learning and mathematics 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
Sponsored by: Darktrace
