Scott Durand

Marketing Manager at Axellio Inc.

Scott Durand has a diverse work experience in marketing and digital coordination. Scott worked as a Marketing Manager and Marketing Specialist at Axellio Inc. and BEI | Exit Planning, respectively. At Optiv Inc, they worked as a Digital Marketing Coordinator, developing and executing on creative digital assets and platform-specific copy. As a Social Media Coordinator and Project Manager, they managed social media campaigns and website development at LTC Expert Publications on a contract basis. Additionally, they gained experience in email campaign management and data analysis as a Digital Commerce Specialist at New Balance and a Marketing Specialist at One Link Software. Finally, starting their career as an Operations and Compliance Intern at the Office of US Senator Kay R. Hagan, they reviewed and processed campaign contributions.

Scott Durand graduated from Charlotte Christian High School with a High School Diploma in 2011. Scott then received a Bachelor of Science degree in Marketing from Elon University in 2015. Additionally, Scott has obtained several certifications from AttackIQ in December 2021 in the fields of Breach & Attack Simulation, Operationalizing MITRE ATT&CK, Purple Teaming, and Strategic Cybersecurity Management.

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Denver, United States

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Axellio Inc.

Axellio® is an innovator in network intelligence platforms for the Department of Defense, the Intelligence Community, and global security operations. Axellio’s PacketXpress® is the highest performing, smallest form factor network traffic capture, storage, analysis, and distribution platform, combining the benefits of network packet recorder and packet brokers. This open, scalable, and application agnostic platform has the unique ability of simultaneous on-disk recording and off-disk distribution at well over 100 Gbps with no loss. This allows any cyber security monitoring and forensic analysis application to close the visibility gap, increasing operational effectiveness for rapid and informed decisions, while reducing the total cost of ownership and analysis complexity.