The value of using AI for network observability is the ability to take massive amounts of data about your service health and identify What, Where, When, and Why went wrong, letting people with less expertise on your team to root-cause issues and be proactive. While MELT data and flow-data is readily available it’s limited to identifying issues after they happen (known-unknowns) and can miss unanticipated issues, or “unknown unknowns”, that impact network health and security. Only cPacket’s AI-driven observability platform leverages packet-based data at line-rate performance and high precision so your organization can be proactive and identify unknown-unknown issues. cPacket delivers deeper visibility for health optimization and comprehensive security monitoring, empowering organizations to uncover hidden issues and enhance network resilience in complex IT environments.
Ron brings more than 20 years of experience leading engineering teams through the
creation and development of complex networking. Ron holds a B.Sc. in Electrical and
Computer Engineering from the Technion in Israel and holds more than 15 granted US
patents.