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  • Company (that provides the nominated product / solution / service): SlashNext
  • Website: http://www.slashnext.com
  • Company size (employees): 10 to 49
  • Country: United States
  • Type of solution: Software
  • Approximate number of users worldwide: 50+

What other awards did this nomination receive in the previous 12 months?

None, as the product was introduced in November 2017

In 3 bullets, summarize why this product or service is different from the competition and deserves recognition:

• While many vendors are trying to take legacy solutions and "snap on" artificial intelligence or machine learning capabilities, SlashNext was designed from the ground-up to simulate and automate the work of a thousand researchers. Everything the system does is based on a human action, including what they would look for or what they would do to test their findings. The major difference is that those actions are performed by smart machines who continue to learn based on previous decisions and results.
• The SlashNext service is designed for any organization who doesn't have dozens of dedicated security researchers. Our mission is to alleviate the IT team who needs to uplevel their security posture with advanced threat detection for their employees.
• The SlashNext service is easy to deploy - in fact, our promise is that the system is up and running in 20 minutes. We also know we detect things other systems are not catching. Within the first 30 days, if we don't catch something that a sandbox or other threat detection tool missed, we won't charge a penny for the service!

Description

The SlashNext Internet Access Protection system protects unsuspecting employees from accidently engaging in social engineering or other sophisticated threats which could lead to full-scale cyber-attacks. SlashNext automates the work of thousands of threat researchers by detecting and blocking malware and malware-free exploits missed by network-based, signature-based and sandbox-based technologies. Using a patent-pending cognitive computing engine in the cloud, gigabits of Internet-bound traffic are broken up into a set of artifacts, telltale signs of a malicious attack. These artifacts are converted into clear Indicators of Compromise (IOCs) which are handed over to hundreds of reasoning “agents” that behave like a team of threat researchers, each rapidly testing, judging and reporting an assessment of their specific task. All results are weighted and combined to reach a single verdict, “100% Malicious” or “Not Malicious.” Once a decision is made, the final outcome is shared back across the cognitive system as part of a peer feedback mechanism which gives the system its unique reinforced-learning capability. This process is a huge contrast to machine learning-based systems that need to be manually trained repeatedly by data scientists and an exact replication of a team of human threat researchers who process raw data, compile evidence, analyze using cognition, discuss and then collectively reach a decision. The SlashNext system is deployed via a simple, 20-minute installation process that requires zero policy configuration or ongoing maintenance.

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