Overview

The exponential growth of zero-day threats on a daily basis and the absence of a comprehensive solution to handle APT attacks has caused companies to follow a “not if but when” approach to breaches. Many solutions only protect a single part of the enterprise’s cybersecurity ecosystem, leaving critical gaps in overall protection. Legacy solutions generate reactive, time-consuming handcraft signatures. Newer sandboxing solutions or even machine learning-based solutions provide more sophisticated solutions but their detection capabilities, albeit higher, are still lacking. With the evolution of hackers’ abilities, enterprises’ reliance on post-attack investigation and incident response plans cannot detect new attacks before the damage is done.

Deep Instinct is the first company to apply deep learning to cybersecurity. Deep learning is inspired by the brain’s ability to learn. Once a brain learns to identify an object, its identification becomes second nature. Similarly, as Deep Instinct artificial brain learns to detect any type of cyber threat, its prediction capabilities become instinctive. As a result, zero-day and APT attacks are prevented in real-time with unmatched accuracy. Deep Instinct brings a completely new approach to cybersecurity that is proactive and data-agnostic. Deep Instinct provides comprehensive defense that is designed to protect against known and unknown malware in real-time, across an organization’s endpoints, servers, and mobile devices. Deep learning’s capabilities of identifying malware from any data source results in comprehensive protection on any device, platform, and operating system, filling in gaps of niche solutions.
Deep Instinct launched its commercially available product on November 3, 2015. It is currently available for large enterprises in various industries such as financial institutions, healthcare organizations, telecommunications and ecommerce.