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Resilience - The Modern Uptime Trinity

Terry Critchley

Some years ago, the computer systems' key focus was on performance and many articles, products and efforts were evident in this area. A few years later, the emphasis moved to high availability (HA) of hardware and software and all the other machinations they entail. Today the focus is on (cyber)security.

Read Dr. Terry Critchley's full paper on Resilience

These discrete environments' boundaries have now blurred under the heading of resilience. The main components of resilience are:

1. Normal high availability (HA) design, redundancy etc. plus normal recovery from non-critical outages. This applies to hardware and software. Human factors ("fat finger" syndrome and deliberate malice), are extremely common causes of failure.

2. Cybersecurity breaches of all kinds. No hard system failures here but leaving a compromised system online is dangerous. This area has spawned the phrase cybersecurity resilience.

3. Disaster Recovery (DR), a discipline not in evidence, for example, in May 2017 when Wannacry struck the UK NHS (National Health Service).

You can't choose which of the three bases you cover; it's all or nothing and in the "any-2-from-3" choice, disaster beckons. It would be like trying to build then sit on a two-legged stool.

In boxing, resilience in simple terms means the ability to recover from a punch (normal recovery) or knock down (disaster recovery). However, it has connotations beyond just that, inasmuch as the boxer must prepare himself via tough training, a fight plan and coaching to avoid the knockdown and, should it happen, he should be fit enough to recover and re-join the fray quickly enough to beat the 10 second count; financial penalties in our world.

When is an Outage Not an Outage?

This is a valid question to ask if you understand service level agreements (SLAs). SLAs specify what properties the service should offer aside from a "system availability clause." These requirements usually include response times, hours of service schedule (not the same as availability) at various points in the calendar, for example, high volume activity periods such as major holidays, product promotions, year-end processing and so on.

Many people think of a system outage as complete failure — a knockout using our earlier analogy. In reality, a system not performing as expected and defined in a Service Level Agreement (SLA) will often lead users to consider the system as ‘down' since it is not doing what it is supposed to do and impedes their work.

This leads to the concept of a logical outage(a forced standing count in boxing) where physically everything is in working order but the service provided is not acceptable for some reason. These reasons vary, depending at what stage the applications have reached but they are many.

Resilience Areas

Resilience in bare terms means the ability to recover from a knock down, to use the boxing analogy once more. However, it has connotations beyond just that inasmuch as the boxer must prepare himself by tough training and coaching to avoid the knockdown and, should it happen, he should be fit enough to recover, get to his feet and continue fighting. The information technology (IT) scenario this involves, among other things:

■ "Fitness" through rigorous system design, implementation and monitoring.

■ Normal backup and recovery after outages or data loss.

■ Cybersecurity tools and techniques.

■ Disaster Recovery (DR) when the primary system(s) is totally unable to function for whatever reason and workload must be located and accessed from facilities — system and accommodation (often forgotten) — elsewhere.

■ Spanning the resilience ecosphere are the monitoring, management and analysis methods to turn data into information to support the resilience aims of a company and improve it. If you can't measure it, you can't manage it.

Figure 1 is a simple representation of resilience and the main thing to remember is that it is not a pick and choose exercise; you have to do them all to close the loop between the three contributing areas of resilience planning and recovery activities.


Figure 1: Resilience Components

Security(cybersecurity) is a new threat which the business world has to be aware of and take action on, not following the Mark Twain dictum: "Everybody is talking about the weather, nobody is doing anything about it."

The key factor is covering all the ‘resilience' bases at a level matching the business's needs. It is not a "chose any n from M" menu type of choice; it is all or nothing for optimum resilience.

To stretch a point a little, I think that resilience will be enhanced by recognizing the "trinity" aspect of the factors affecting resilience and should operate as such, even in virtual team mode across the individual teams involved. This needs some thought but a "war room" mentality might be appropriate.

The three areas considered in parallel (P) make for a more resilient system than different teams treating them in isolation as serial or siloed activities (S). Another downside of S is that it requires three sets of change management activities.

Conclusion

Like any major activity, the results of any resilience plan need review and corrective action taken. This requires an environment where parameters relating to resilience are measurable, recorded, reviewed and acted upon; it is not simply a monitoring activity since monitoring is passive, management is active and proactive.

Management = Monitoring + Analysis + Review + Action

This is a big subject which few understand in size or complexity but it has to be tackled.

Resilience is hard. If you think that throwing suitable, trendy products at the resilience design is the answer, you are deluding yourself. As Sir Winston Churchill said, in paraphrase; "All I can offer is blood, sweat and tears."

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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.

Resilience - The Modern Uptime Trinity

Terry Critchley

Some years ago, the computer systems' key focus was on performance and many articles, products and efforts were evident in this area. A few years later, the emphasis moved to high availability (HA) of hardware and software and all the other machinations they entail. Today the focus is on (cyber)security.

Read Dr. Terry Critchley's full paper on Resilience

These discrete environments' boundaries have now blurred under the heading of resilience. The main components of resilience are:

1. Normal high availability (HA) design, redundancy etc. plus normal recovery from non-critical outages. This applies to hardware and software. Human factors ("fat finger" syndrome and deliberate malice), are extremely common causes of failure.

2. Cybersecurity breaches of all kinds. No hard system failures here but leaving a compromised system online is dangerous. This area has spawned the phrase cybersecurity resilience.

3. Disaster Recovery (DR), a discipline not in evidence, for example, in May 2017 when Wannacry struck the UK NHS (National Health Service).

You can't choose which of the three bases you cover; it's all or nothing and in the "any-2-from-3" choice, disaster beckons. It would be like trying to build then sit on a two-legged stool.

In boxing, resilience in simple terms means the ability to recover from a punch (normal recovery) or knock down (disaster recovery). However, it has connotations beyond just that, inasmuch as the boxer must prepare himself via tough training, a fight plan and coaching to avoid the knockdown and, should it happen, he should be fit enough to recover and re-join the fray quickly enough to beat the 10 second count; financial penalties in our world.

When is an Outage Not an Outage?

This is a valid question to ask if you understand service level agreements (SLAs). SLAs specify what properties the service should offer aside from a "system availability clause." These requirements usually include response times, hours of service schedule (not the same as availability) at various points in the calendar, for example, high volume activity periods such as major holidays, product promotions, year-end processing and so on.

Many people think of a system outage as complete failure — a knockout using our earlier analogy. In reality, a system not performing as expected and defined in a Service Level Agreement (SLA) will often lead users to consider the system as ‘down' since it is not doing what it is supposed to do and impedes their work.

This leads to the concept of a logical outage(a forced standing count in boxing) where physically everything is in working order but the service provided is not acceptable for some reason. These reasons vary, depending at what stage the applications have reached but they are many.

Resilience Areas

Resilience in bare terms means the ability to recover from a knock down, to use the boxing analogy once more. However, it has connotations beyond just that inasmuch as the boxer must prepare himself by tough training and coaching to avoid the knockdown and, should it happen, he should be fit enough to recover, get to his feet and continue fighting. The information technology (IT) scenario this involves, among other things:

■ "Fitness" through rigorous system design, implementation and monitoring.

■ Normal backup and recovery after outages or data loss.

■ Cybersecurity tools and techniques.

■ Disaster Recovery (DR) when the primary system(s) is totally unable to function for whatever reason and workload must be located and accessed from facilities — system and accommodation (often forgotten) — elsewhere.

■ Spanning the resilience ecosphere are the monitoring, management and analysis methods to turn data into information to support the resilience aims of a company and improve it. If you can't measure it, you can't manage it.

Figure 1 is a simple representation of resilience and the main thing to remember is that it is not a pick and choose exercise; you have to do them all to close the loop between the three contributing areas of resilience planning and recovery activities.


Figure 1: Resilience Components

Security(cybersecurity) is a new threat which the business world has to be aware of and take action on, not following the Mark Twain dictum: "Everybody is talking about the weather, nobody is doing anything about it."

The key factor is covering all the ‘resilience' bases at a level matching the business's needs. It is not a "chose any n from M" menu type of choice; it is all or nothing for optimum resilience.

To stretch a point a little, I think that resilience will be enhanced by recognizing the "trinity" aspect of the factors affecting resilience and should operate as such, even in virtual team mode across the individual teams involved. This needs some thought but a "war room" mentality might be appropriate.

The three areas considered in parallel (P) make for a more resilient system than different teams treating them in isolation as serial or siloed activities (S). Another downside of S is that it requires three sets of change management activities.

Conclusion

Like any major activity, the results of any resilience plan need review and corrective action taken. This requires an environment where parameters relating to resilience are measurable, recorded, reviewed and acted upon; it is not simply a monitoring activity since monitoring is passive, management is active and proactive.

Management = Monitoring + Analysis + Review + Action

This is a big subject which few understand in size or complexity but it has to be tackled.

Resilience is hard. If you think that throwing suitable, trendy products at the resilience design is the answer, you are deluding yourself. As Sir Winston Churchill said, in paraphrase; "All I can offer is blood, sweat and tears."

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.