Zscaler
Case Studies
Goulston & Storrs Enhances Client Data Security with Zscaler Workload Segmentation
Overview
Goulston & Storrs Enhances Client Data Security with Zscaler Workload SegmentationZscaler |
Analytics & Modeling - Machine Learning Cybersecurity & Privacy - Network Security | |
National Security & Defense Telecommunications | |
Maintenance | |
Inventory Management Tamper Detection | |
Cloud Planning, Design & Implementation Services Data Science Services | |
Operational Impact
The implementation of Zscaler Workload Segmentation has resulted in several operational benefits for Goulston & Storrs. The firm has experienced a significant reduction in the risk of client data exfiltration. Operational efficiencies have been created by machine learning, reducing work associated with protection policy creation and management. The firm no longer has to balance security and business agility priorities. With one-click enforcement of automatically generated policies, the time it takes to implement zero trust has been drastically reduced. The ease of implementation and maintenance of Zscaler Workload Segmentation compared to other security deployments has made it an obvious choice for the firm. | |
Quantitative Benefit
Reduced the network attack surface to cover 99.99 percent with optimal protection policies | |
Implemented Zscaler Workload Segmentation without requiring changes to applications or network infrastructure, leading to faster time-to-value | |
Machine learning automatically modeled the firm’s application communication patterns in less than 72 hours | |