Complete coverage and insight at scale
The Cynamics Mission
We help organizations protect their networks and stay safe. By providing threat prediction and visibility quickly and at scale, we deliver elite cyber defense to networks of any size and complexity. Cynamics is the only Cloud-Based network detection and response solution that uses standard sampling
protocols that built in to every gateway, providing organizations with an advanced technology capabilities, with minimal burden on their resources.
The Team
The Cynamics team is comprised of experienced intelligence and technology experts from a wide variety
of disciplines, including cyber intelligence and warfare, government security, AI, machine learning, and more. Our diverse, multi-national team is committed to providing the highest level of network security to organizations, so they can focus on their operations and thrive.
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We are shaping the future –
a scalable, cost-effective network visibility solution. Our young dynamic team is agile and bright. We approach our work-life balance just as seriously as we tackle the latest technological challenge.
Cynamics’ Development
2019
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Cynamics launch
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Israel HQ opening
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First US clients
2021
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$7M in Funding
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4th Patent
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Boston HQ opening
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Presented Technical AI Papers at:
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IETF
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ACM CCS AISec 21’
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2023
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Industry Recognitions:
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CRN Stellar Startup
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​‘Top Performer’ in Gartner VoC
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GigaOM NDR report
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Cynamics Federal with Merlin launch
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Cynamics AI in the edge with Nvidia launch
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Cynamics & Nvidia IP filing
2020
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3 Patents​​
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Cynamics AI Network Blueprint launch
2022
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Cloud capabilities
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Dedicated MSSP product launch
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Industry Recognitions:
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Gartner’s Guide for NDR
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'Distinguished Vendor' by TAG Cyber
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Two IP filings
Patents
#1
Registration No. 11,711,310
Inferring 100% network coverage from small samples
#2
Publication No. US-2022-0053010-A1
Predicting Ransomware attacks
#4
Publication No. US-2023-0090205-A1
Predicting endpoint threats without an agent
#5
Publication No. US-2024-0015134-A1
Predicting network assets from small network samples
#3
Registration No. 11,716,338
Predicting threats using small sample