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|Company size (employees)||10 to 49|
|Headquarters Region||North America|
In 3 bullets, summarize why this company 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.
Summary of Achievements
In the past year, SlashNext received its first round of funding and launched the SlashNext Internet Access Protection System. The system blocks social engineering and phishing attacks by putting a human-like machine against attackers to protect organizations from cross platform social engineering and phishing, malware, exploits and callback attacks. The SlashNext system goes beyond previous generations, including network-based, signature-based and sandbox-based technologies by deploying human-like intelligence and cognitive thinking to stop Internet attacks that target unsuspecting employees as their entry points. The SlashNext solution is deployed via a simple, 20-minute installation process that requires zero policy configuration or ongoing maintenance. Once installed, the system employs a patent pending threat protection technology that includes a cross platform protocol analysis engine that processes gigabits of Internet-bound traffic in real-time to extract a complex set of artifacts. These artifacts are essentially the telltale signs of a malicious attack. The artifacts are further processed by a cognitive computing machine that uses massive cloud computing power to convert these features into clear Indicators of Compromise (IOCs). The IOCs are then handed over to hundreds of reasoning engines that behave like a team of decision-makers working together to reach a single verdict, “100% Malicious” or “Not Malicious.” Once a decision is made, the final outcome is shared back with all the decision makers as part of a peer feedback mechanism that gives the system its unique self-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.