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.
Apply adaptive and simplified policies to allow only verified workloads to communicate over approved pathways. Never trust, always verify.
Identify data stores and map communication pathways to understand your security risk. Prioritize protection based on risk of compromise.
Enable DevOps and SREs to build and deploy software with more security and with fewer disruptions to the SDLC. Machine-learned policy creation and enforcement allows auto-scaling in even the most elastic cloud environment.