AvionX Inc

AvionX Inc is located in Cupertino, United States on 19925 Stevens Creek Blvd #100. AvionX Inc is rated 3 out of 5 in the category computer & network security in United States. The first and foremost mission of AvionX is to help Enterprise SOCs to respond to threats by reducing noise from their security products in their existing environments and analyzing the incidents which matter the most in a timely fashion. We at AvionX believe that we can significantly reduce the risks of cyber attacks, and protect customers from cyber-criminals who are trying to exploit infrastructure vulnerabilities, and are capable of weaponizing malware, botnets etc. to formulate targeted attacks. In spite of the presence of many security products in the enterprise, the challenge still remains for the customer to ensure that their data and infrastructure is secured to avoid any data breaches. AvionX believes that cyber attacks can be minimized by bringing products, big data analytics and cyber professionals together to solve this complex problem. AvionX is all set to arm cyber data analysts with deep learning algorithms and qualified cyber professionals to help them with the overwhelming number of alerts that are received from the various security appliances. As we all know, most alerts are common and a nuisance. Adware, for example, has little significance, but these types of alerts must also be processed amongst other real dangerous malware and spyware. AvionX uses a threat detection platform to solve this complex problem to provide contextual meaningful information for responding to alerts to the subject matter experts to identify incidents. The goal of AvionX is to Improve the efficiency of human security analyst by utilizing advanced Machine Learning techniques Highlight known and evolving common threat vectors Enable human analysts to focus on more interesting and complex cyber threats and targeted attacks which are difficult to analyze and remediate. Accelerate and highlight potential zero-day threats through deep learning techniques and achieve predictive analytics