cPacket Proactive Service Assurance and Compliance

Latency issues don’t always wait for end users to notice and neither should your operations team. In this session, cPacket demonstrated how they enable proactive latency detection using leading indicators, full-path packet monitoring, and anomaly detection. With integrations into LLM-powered workflows and platforms like Slack and ITSM, teams can resolve issues faster, tune alerts more precisely, and continuously improve visibility through real-time data and trend reporting. The core focus was on achieving proactive service assurance, shifting from a reactive “firefighting” model to one where issues are identified and resolved before they impact users, ideally reducing human-created incidents.

Ron Nevo elaborated on this “nirvana” state, where network operators can proactively assess network health using cPacket’s Observability AI. The system processes trillions of packets to distill vast amounts of data into a manageable handful of “insights,” highlighting what’s most important for a specific operator’s responsibilities. A key use case demonstrated this: querying the system for new insights over the past 24 hours. The LLM (Large Language Model) identified client latency issues and resource utilization problems on a core engineering server. While the interaction still requires a certain level of network engineering sophistication to interpret the insights, the goal is to simplify the discovery process and guide operators to critical areas.

cPacket’s approach relies on dynamic baselining, where the AI learns normal network behavior over time across various metrics and services, detecting anomalies that might indicate a problem before an outage occurs. While the presented prompts were complex, the long-term vision is to abstract this complexity, making the system more intuitive and capable of providing precise, actionable guidance. The ultimate value lies in accelerating the triage process, shortening the Mean Time To Detect (MTTD) and Mean Time To Resolve (MTTR) by integrating AI-driven insights with existing workflows and tools like Slack and ServiceNow. This approach aims to augment human operators, providing them with a powerful tool to proactively manage the network and ensure continuous service reliability.