Visualize Workload Risk

Visualize Workload Risk

Reduce your attack surface and expose risk with Zero Trust.

Why Edgewise

Only allow trusted software to communicate.
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Agentless protection for serverless workloads.
No policy changes needed after migration.
Reveal risk for specific applications or hosts

Reveal risk for specific applications or hosts

Easily filter by applications or hosts of interest and see the exposure assessment report. Edgewise verifies the secure identities of communicating workloads using the Zero Trust model.

Review overall exposure

Review overall exposure of applications or hosts in your environment

Quantify network paths beyond those necessary for business services to perform their business function. Understand how many application communication pathways currently protected, could be protected with available policies, or have no protection policies available. Compare the portion of used application communication pathways currently protected by Edgewise, versus the potential protection if all machine learning generated rules were applied.

Evaluate exposure of specific applications

Evaluate exposure of specific applications or hosts in your environment to prioritize remediation

Review the network pathways that the business application or host don’t need to operate. These potential attack paths can be eliminated. Quantify the pathways that can be protected with Zero Trust policies that allow only verified applications, users and hosts to communicate. Deliver an exposure report to the larger team.


Edgewise Networks
Edgewise Protect

Stop Data Breaches with Zero Trust

Apply adaptive and simplified policies to allow only verified workloads to communicate over approved pathways. Never trust, always verify.

Automatically Measure Risk

Identify data stores and map communication pathways to understand your security risk. Prioritize protection based on risk of compromise.

Enable DevOps Security

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.