The first step in speeding up microsegmentation projects is to understand how applications communicate and quantify their network attack surface. Edgewise automatically maps application topologies to reveal how applications communicate and then highlights potential pathways of attack within the network. This Zero Trust view helps defenders gain a clear picture of the applications they need to protect and prioritize based on exposure risk.
Edgewise uses machine learning to build Zero Trust policies that provide the broadest coverage with the fewest number of policies. The machine learning system quantifies the reduction in network attack surface and exposure risk realized by applying Edgewise policies. Edgewise produces plain-English policies, making it easy for application owners, security operations, and networking teams to collaborate more easily—no need to translate “application speak” to “network speak.”
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.