Skip to main content

How to Prepare for Your Next Network War Room Debate - Part 1

Jay Botelho

The term "war room" comes from military: a place to strategize and debate. Over the years this concept has been heavily adopted by others, from NFL teams drafting the next star player to software developers rushing to fix code flaws. Given the broad management challenges associated with network operations, it's no surprise that war rooms have become commonplace in IT. Rolling out a new ERP system? To the war room for team collaboration and planning. Critical financial service just crashed? The war room is the place to be for some dramatic finger pointing and lightning-fast problem resolution.

For anyone that's been in a war room, there's no denying that it can be an intense place. Depending on what side of the table you're on it can be teeming with confidence and dominance, or veiled in defensiveness and uncertainty. Teams go to the war room to win. But, the ideal outcome is a solid plan or solution designed to deliver the best outcome while utilizing the least resources.

What are some of the key triggers that drive IT teams into the war room and how can you prepare yourself to contribute in a positive way?

Most war room sessions are either project-focused or response-focused. For example, project-focused war room triggers could be an infrastructure migration for software-defined networks or a shift to a hybrid cloud environment. Other possible triggers include large digital transformation initiatives that require major applications rollouts, such as a 100 percent commitment to going paperless through digitization and immediate access to electronic records, or a major ERP consolidation after a corporate merger.

On the response side, the pool of triggers can be vast. For example:

■ A major network or service outage. Imagine your hosted infrastructure provider goes down – yes, this has even happened to AWS.

■ A security incident or potential intrusion. For example, imagine getting a call from the FBI saying that a recent nationwide investigation into credit card fraud has turned up your company as the common denominator.

■ Investigating a serious security breach, such as one that exploited a known vulnerability that you failed to patch, and resulted in tens of millions of records stolen.

Imagine what the war room was like when Equifax was breached back in 2017. Or, when Salesforce had its major cloud outage in 2016, or when Slack experiences continual service outages. What about GDPR? Imagine the war room planning sessions that occurred as the deadline crept closer and closer. That's one you probably don't need to imagine; it's likely you lived through it.

Whether proactive or reactive in nature, war room sessions fundamentally come down to problem solving. But the ultimate goal is to eliminate these sessions altogether. Teams work hard to find problems before they impact the network. It's often called creating a "standard of visibility." They use solutions and tools that offer full system monitoring and visibility (NPMD), with stored data for historical reference; regular and disciplined penetration testing to catch misconfigurations, vulnerabilities and more; cyber security systems to detect hacks and vulnerabilities; and full network packet capture for troubleshooting network problems in real-time.

Read How to Prepare for Your Next Network War Room Debate - Part 2, offering tips on how to win in the war room.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

How to Prepare for Your Next Network War Room Debate - Part 1

Jay Botelho

The term "war room" comes from military: a place to strategize and debate. Over the years this concept has been heavily adopted by others, from NFL teams drafting the next star player to software developers rushing to fix code flaws. Given the broad management challenges associated with network operations, it's no surprise that war rooms have become commonplace in IT. Rolling out a new ERP system? To the war room for team collaboration and planning. Critical financial service just crashed? The war room is the place to be for some dramatic finger pointing and lightning-fast problem resolution.

For anyone that's been in a war room, there's no denying that it can be an intense place. Depending on what side of the table you're on it can be teeming with confidence and dominance, or veiled in defensiveness and uncertainty. Teams go to the war room to win. But, the ideal outcome is a solid plan or solution designed to deliver the best outcome while utilizing the least resources.

What are some of the key triggers that drive IT teams into the war room and how can you prepare yourself to contribute in a positive way?

Most war room sessions are either project-focused or response-focused. For example, project-focused war room triggers could be an infrastructure migration for software-defined networks or a shift to a hybrid cloud environment. Other possible triggers include large digital transformation initiatives that require major applications rollouts, such as a 100 percent commitment to going paperless through digitization and immediate access to electronic records, or a major ERP consolidation after a corporate merger.

On the response side, the pool of triggers can be vast. For example:

■ A major network or service outage. Imagine your hosted infrastructure provider goes down – yes, this has even happened to AWS.

■ A security incident or potential intrusion. For example, imagine getting a call from the FBI saying that a recent nationwide investigation into credit card fraud has turned up your company as the common denominator.

■ Investigating a serious security breach, such as one that exploited a known vulnerability that you failed to patch, and resulted in tens of millions of records stolen.

Imagine what the war room was like when Equifax was breached back in 2017. Or, when Salesforce had its major cloud outage in 2016, or when Slack experiences continual service outages. What about GDPR? Imagine the war room planning sessions that occurred as the deadline crept closer and closer. That's one you probably don't need to imagine; it's likely you lived through it.

Whether proactive or reactive in nature, war room sessions fundamentally come down to problem solving. But the ultimate goal is to eliminate these sessions altogether. Teams work hard to find problems before they impact the network. It's often called creating a "standard of visibility." They use solutions and tools that offer full system monitoring and visibility (NPMD), with stored data for historical reference; regular and disciplined penetration testing to catch misconfigurations, vulnerabilities and more; cyber security systems to detect hacks and vulnerabilities; and full network packet capture for troubleshooting network problems in real-time.

Read How to Prepare for Your Next Network War Room Debate - Part 2, offering tips on how to win in the war room.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.