Unlike a perimeter-based model, a zero trust network adjusts your threat model to assume that your internal network is a hostile network (with attackers probing systems, looking for weaknesses, and trying to gain access). In this hostile network, every device, user, and network flow needs to be authenticated and authorized. To achieve this, Edgewise uses machine learning to model the environment, identify required communication paths, and verify the secure identity of communicating entities.
Zero trust networking from Edgewise abandons IP-based policies and instead builds identity-based policies by verifying the secure identity of workloads, hosts, and users. To ensure a highly secure environment, Edgewise policies are calculated from as many sources of data as possible, including application flows. Edgewise applies machine learning to build the optimal protection policies for an environment, generating the smallest set of policies that offer the broadest protection. Edgewise dynamically scales with your workloads to support the largest environments with consistent security and performance.
Stop lateral movement of malicious software that bypasses firewalls. Lock down your cloud and allow only verified applications to communicate over approved pathways. Receive alerts for any anomalous communication.
As much as 95% of network pathways are not required for normal business use. Eliminate unneeded application communication paths and protect the rest by mutually validating connections before a single packet is sent.
Apply workload protection policies in minutes, not days or months. Quickly approve machine-learned and automatically-built policy recommendations.