Abstract

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.

Speaker

Ms. Cat So
Country Manager of Hong Kong and Macau
Darktrace

Cat So is a Cyber Security Account Manager at Darktrace. With the rapid adoption of Darktrace’s multi-award-winning Enterprise Immune System technology, Cat has been instrumental in laying the foundations for the company’s rapid growth for Hong Kong. Cat has over 10 years of experience in the enterprise software industry, focusing on strategic sales and business development in both the Hong Kong and Taiwan regions. She has held senior roles at Master Concept, Automated Systems and EMC. Cat has a bachelor degree in Software Engineering from Queensland University of Technology.