We Prevent >99% of Unknown Attacks.
Traditional endpoint protection platforms (EPP) tools that rely on rules and signatures are not enough to prevent unknown, zero-day, and new malware variants. Our prevention-first approach to unknown threats is built on a one-of-a-kind deep learning foundation.
Avoid the Impact of a Breach.
Data Breaches Averaged $3.86M in 2020*
Security breaches are becoming more frequent – and more costly. Embrace a future driven by a prevention-first approach where known and unknown threats are stopped before your environment has been compromised.
Stop Unknown Threats, Faster.
Reduce Risk with Deep Learning.
Our multi-layered approach to prevention expands what is possible in cybersecurity. Basic ML is prone to risk as a result of human-led engineering on a subset of data. Deep Instinct’s automated static analysis is based on raw data, allowing for broad protection on the widest variety of threats and file types without human intervention.
Reduce False Positives.
Boost Security Team Efficiency.
Cybersecurity teams spend an average of 9 hours, 40 minutes each week dealing with alerts caused by false positives. Excessive alerts flagged by traditional EPP tools leaves many security teams investigating the wrong events while losing time and focus. Deep Instinct’s fast classification and accurate decision-making reduces false positives to <0.1%, allowing teams to devote more time to critical projects that will improve and harden their security posture.
Tested and Validated.
Independent Tests Confirm >99% Zero-Day Prevention
Two-month testing of Deep Instinct’s prevention accuracy show near perfect detection rates. The tests were based on portable, unknown, custom designed attacks, and Python executables, as well as static, dynamic, network, behavioral analysis, and signature detection.