Aviz Networks Blogs

In today's era, enterprise and telecom networks span much wider than a data center. These networks' traffic traverse through the cloud, edge, hybrid infrastructure, and wireless networks, giving network administrators a challenging time in gaining end-to-end visibility. Lack of smart traffic analysis has put security and network teams' fragmented views and increased incident response times. Typical issues faced are: The inability of getting visibility across a distributed environment. Replication of traffic which impacts data processing costs. Cost-intensive hardware appliances tied to proprietary technology. Lack of data to feed the AI-based observability platforms. Aviz Service Node v2.4 processes raw packet traffic to create machine-readable, application-aware metadata which is consumable by present-day observability platforms. ASN, through deep packet inspection (DPI) and application classification, enhances packet-aware traffic data with context so that AI/ ML can eff...

Modern AI infrastructure no longer only means networking together a stack of GPUs with a bunch of switches. The modern AI factory now relies on tightly optimized networking in which a slight configuration miss will affect performance, price or deployment timeline. Traditionally, this means it requires setting up and testing the network configuration in a physical lab which is expensive, time consuming and not easily scalable. Why, you ask? Because hardware testing alone can be cost prohibitive. Design cycles are serial and iterative, meaning designs and validation are split into distinct phases that take weeks or days to cycle through. When you're designing for the future, you have configuration misses which aren’t caught until you’ve rolled out production. It’s time to disrupt the cycle with digital twins. What NVIDIA AIR and Aviz ONES give you NVIDIA AIR provides an all-digital, virtualized AI network simulation where you can simulate all the switches, links, automation and confi...

Many hackers are able to breach various parts of the healthcare system because the SecOps team is unable to quickly understand how far an attacker can spread throughout multiple systems. Hospitals, clinics, telemedicine, cloud services, devices and facility systems create traffic on a common network; however, due to lack of central control over how traffic flows & devices in the Silverado system, many of these facilities have blind spots pertaining to the analysis of East to West traffic, devices that are not monitored (unmanaged) & mixed infrastructure (devices that do not have agent-based security monitoring). Some examples of this are: Almost all types of ransomware operated by transferring themselves around to other locations before they perform file&folder encryptions. A lot of the different device types and workloads out there within the Healthcare ecosystem will not be able to utilize endpoint agents. Packet level evidence will assist with identifying unusual flows o...

In today's world of healthcare, thousands of connected devices rely on one another, such as infusion pumps, imaging equipment, badge readers, controllers of H/VAC, clinical stations and tele-health endpoints. A majority of these types of devices do not support security agents, can't be patched and were not designed with cybersecurity in mind; they were simply designed to provide safe reliable clinical care.  Here are some real-world observations related to the above statement: • There are substantial amounts of valuable traffic signals that are generated on connected medial and facility devices • Many of the devices cannot be secured by the use of traditional endpoint controls • Packet-level visibility can provide insight into device behaviour patterns, communications, and risk • Optimized delivery of traffic helps improve the accuracy of asset discovery and monitoring tools • Network evidence can support security compliance and incident investigation processes There is a comm...