Galvanick
Brandon Park has a diverse work experience in the field of technology and national security. Brandon currently serves as the CTO & Co-Founder at Galvanick since August 2021. Prior to that, they worked as a Security Engineer - Industrial Control Systems at Amazon from January 2018 to July 2021. Brandon also held the role of Senior Engineer - ICS Cybersecurity Lab at Bechtel Nuclear, Security & Environmental, Inc. from 2016 to 2018. Furthermore, Brandon worked at Merck & Co. as an Automation Engineer - Industrial Control Systems from 2009 to 2016. Additionally, they have been involved in academic work as a Technologist Fellow at George Mason University - Antonin Scalia Law School's National Security Institute since 2020 and as a Visiting Fellow at the National Security Institute (NSI) from September 2022.
Brandon Park attended the University of Pennsylvania from 2005 to 2009, where they studied Electrical Engineering. Brandon obtained several certifications, including the GIAC Certified Detection Analyst (GCDA) from GIAC Certifications in October 2021, the GIAC Certified Penetration Tester (GPEN) in April 2021, the GIAC Network Forensic Analyst (GNFA) in October 2019, and the GIAC Advisory Board and GIAC Response and Industrial Defense (GRID) certifications in December 2018. Additionally, they obtained the ISA/IEC 62443 Cybersecurity Expert certification from the International Society of Automation (ISA) in March 2017.
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Galvanick
When it comes to securing Industrial Operations environments, we believe humans should always be in the loop as actions in the cyber-physical world have real consequences. Threats are increasing faster than we can hire and train employees. We provide an Intelligence Augmentation platform that acts as your “cyber-sidekick”. Galvanick helpsindustrial companies secure their operations by reducing the time to detection of anomalous behavior on their most sensitive assets. The Galvanick cloud-native analysis platform aggregates telemetry from industrial environments and hosts systemic models that enable operators to quickly understand the difference between, and context around, both normal and abnormal behavior