Skip to main content

LiveAction Packet Capture and Forensics Available on Cisco UCS

LiveAction announced the certification of packet capture and analysis software on high-performance Cisco Unified Computing Systems (UCS) servers.

Cisco UCS integrates computing, networking, and storage into a unified system, providing the essential foundation to drive your applications. Incorporating LiveAction’s packet capture and forensics capabilities, enables customers to obtain superior performance and reliability on UCS chassis as well as faster MTTR for network and security incidents.

LiveAction offers four key technical differentiators that give UCS customers valuable packet data and incident response abilities.

- Storage density; providing 3PB of effective storage in 4 Rack Units, 17x denser than common competitors.

- Enhanced SASE/SSE visibility for Cisco Catalyst SD-WAN combining both performance and security data.

- Bi-directional workflows with Splunk that provide both network observability and forensic deep dive.

- New, out-of-the-box workflows between SNA (formerly Stealthwatch) and LiveWire for forensic investigation.

“With LiveAction packet capture and forensics running on UCS, we’re excited to give Cisco customers next-gen network intelligence and observability on best-in-class hardware,” said Francine Geist, CEO of LiveAction.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

LiveAction Packet Capture and Forensics Available on Cisco UCS

LiveAction announced the certification of packet capture and analysis software on high-performance Cisco Unified Computing Systems (UCS) servers.

Cisco UCS integrates computing, networking, and storage into a unified system, providing the essential foundation to drive your applications. Incorporating LiveAction’s packet capture and forensics capabilities, enables customers to obtain superior performance and reliability on UCS chassis as well as faster MTTR for network and security incidents.

LiveAction offers four key technical differentiators that give UCS customers valuable packet data and incident response abilities.

- Storage density; providing 3PB of effective storage in 4 Rack Units, 17x denser than common competitors.

- Enhanced SASE/SSE visibility for Cisco Catalyst SD-WAN combining both performance and security data.

- Bi-directional workflows with Splunk that provide both network observability and forensic deep dive.

- New, out-of-the-box workflows between SNA (formerly Stealthwatch) and LiveWire for forensic investigation.

“With LiveAction packet capture and forensics running on UCS, we’re excited to give Cisco customers next-gen network intelligence and observability on best-in-class hardware,” said Francine Geist, CEO of LiveAction.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...