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Exploring the Convergence of Observability and Security - Part 6: Challenges

Pete Goldin
APMdigest

With input from industry experts — both analysts and vendors — this 8-part blog series will explore what is driving the convergence of observability and security, the challenges and advantages, and how it may transform the IT landscape.

Start with: Exploring the Convergence of Observability and Security - Part 1

Start with: Exploring the Convergence of Observability and Security - Part 2: Logs, Metrics and Traces

Start with: Exploring the Convergence of Observability and Security - Part 3: Tools

Start with: Exploring the Convergence of Observability and Security - Part 4: Dashboards

Start with: Exploring the Convergence of Observability and Security - Part 5: Teams

If you have already read the previous blogs in this series exploring the convergence of observability and security, the challenges will not surprise you. The experts cite compatibility of tools, teams and cultures as challenges to convergence, among others.

The following are some of the challenges experts see with achieving convergence:

Aversion to Change

Colin Fallwell, Field CTO of Sumo Logic: "Probably the biggest challenge comes down to one word. Change. Most people don't like change, much less transformation. DevSecOps requires change, it requires thinking about transformation as a continuous process that is never-ending. Up until now, this kind of transformation really could not happen, but with the rise of the Cloud Native Computing Foundation, the proliferation of open standards, and the mass adoption of OSS tooling like OpenTelemetry, and the need for proprietary agents for collecting telemetry are at an end, and with them the siloes of data."

Different Cultures

Prashant Prahlad, VP of Cloud Security Products at Datadog: "The biggest roadblock to the convergence of security and observability is culture. Security teams need to be able to trust observability teams with product security and still be able to get the visibility they need as a failsafe."

Different Priorities

Mike Loukides, VP of Emerging Tech Content at O'Reilly Media: "I think the major challenges will be the ones we've had all along. Management wants to deliver a new version on April 1. Development is under the gun to release. Ops is under the gun to deploy. And you'll still have security experts saying: Let's make sure we didn't take any shortcuts writing the code; let's make sure we're tracing the right things. It would be nice if this conflict would go away, but I don't think it will. Not now, not ever. However, putting security and ops teams in the same group will help."

Different Budgets

Kirsten Newcomer, Director, Cloud and DevSecOps Strategy at Red Hat: "The purchasing decision and budgets for observability and security may be in different organizations."

Data Silos

Buddy Brewer, Chief Product Officer at Mezmo: "Currently, many organizations unintentionally lock data in silos that only certain teams can access, which often means DevOps and SecOps teams are either not getting the right data or implementing their individual solutions to get data from the same sources. While converging security and observability will make data significantly more actionable, organizations will be met with challenges with getting the data in the correct formats to be used by different tools they may need. In addition, they must make sure that they are adhering to regulations such as GDPR and CCPA and handle personal identifiable information (PII) properly."

Tool Silos

Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at Enterprise Management Associates (EMA) outlines several challenges to convergence. "First, the teams have separate tools with separate tool silos. Often, when these groups come together, they find the quality of the data collected by the other silo's tools are of poor quality. It's in a format that is useless to them, for instance. Also, there is no authoritative source of data. Both groups have their own data stores that represent the same truth about infrastructure and services, but the data disagrees with each other due to variations and data granularity, time stamping, etc."

"Neither group wants to give up control of tool strategy," McGillicuddy continues. "They're married to their individual tools. Which one will blink and give up their tool in favor of the other group's tool?"

Use the player or download the MP3 below to listen to EMA-APMdigest Podcast Episode 2 — Shamus McGillicuddy talks about Network Observability, the convergence of observability and security, and more.

Click here for a direct MP3 download of Episode 2 - Part 1

"We have a lot of work to do to make the tools work properly, so this is not an easy integration – largely because the observability tools were designed for observability. They were not designed for security purposes," adds Adam Hert, Director of Product at Riverbed.

Legacy Tools

Ajit Sancheti, GM, Falcon LogScale at CrowdStrike: "Legacy logging and event management tools may not provide the scale or the performance to ingest all data, which leads to ingest backlogs and sluggish search speed. Organizations should carefully evaluate logging products before attempting to collect all security and observability data in one tool."

Legacy Philosophies

Jam Leomi, Lead Security Engineer at Honeycomb: "The heart of the challenge in converging the two goes back to the culture shift we're seeing in security. A lot of today's practitioners are stuck in compliance practices or philosophies that are 30+ years old. As technology evolves, our security approach has to shift. This creates an opportunity to really connect security with the overall bottom line of the business instead of just as an afterthought. Observability as a tool and practice has the power to do a lot of the heavy lifting toward this goal, enabling a higher level of efficiency, security, and privacy."

Confidential Data

Kirsten Newcomer from Red Hat: "Some security data is not appropriate for sharing with all team members who need to consume observability data."

Security Experts are hard to find

Prashant Prahlad of Datadog: "Security experts are hard to find and take time to train within DevOps teams, so implementing DevSecOps is a long-term investment."

Knowledge Gap

Asaf Yigal, CTO of Logz.io: "Even for those that desire, or are prone to converge responsibilities, there's still a knowledge gap. Most often this is coming from the DevOps side, as in 'how do we take this important data and communicate effectively to security?' And the answer is: this is an emerging practice, so there's no wrong way, and we are working on the proverbial airplane whilst in flight!"

Despite all these challenges, Chaim Mazal, Chief Security Officer at Gigamon offers a positive outlook: "There are far fewer downsides to this convergence than there are advantages."

Go to: Exploring the Convergence of Observability and Security - Part 7: Advantages

Pete Goldin is Editor and Publisher of APMdigest

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Exploring the Convergence of Observability and Security - Part 6: Challenges

Pete Goldin
APMdigest

With input from industry experts — both analysts and vendors — this 8-part blog series will explore what is driving the convergence of observability and security, the challenges and advantages, and how it may transform the IT landscape.

Start with: Exploring the Convergence of Observability and Security - Part 1

Start with: Exploring the Convergence of Observability and Security - Part 2: Logs, Metrics and Traces

Start with: Exploring the Convergence of Observability and Security - Part 3: Tools

Start with: Exploring the Convergence of Observability and Security - Part 4: Dashboards

Start with: Exploring the Convergence of Observability and Security - Part 5: Teams

If you have already read the previous blogs in this series exploring the convergence of observability and security, the challenges will not surprise you. The experts cite compatibility of tools, teams and cultures as challenges to convergence, among others.

The following are some of the challenges experts see with achieving convergence:

Aversion to Change

Colin Fallwell, Field CTO of Sumo Logic: "Probably the biggest challenge comes down to one word. Change. Most people don't like change, much less transformation. DevSecOps requires change, it requires thinking about transformation as a continuous process that is never-ending. Up until now, this kind of transformation really could not happen, but with the rise of the Cloud Native Computing Foundation, the proliferation of open standards, and the mass adoption of OSS tooling like OpenTelemetry, and the need for proprietary agents for collecting telemetry are at an end, and with them the siloes of data."

Different Cultures

Prashant Prahlad, VP of Cloud Security Products at Datadog: "The biggest roadblock to the convergence of security and observability is culture. Security teams need to be able to trust observability teams with product security and still be able to get the visibility they need as a failsafe."

Different Priorities

Mike Loukides, VP of Emerging Tech Content at O'Reilly Media: "I think the major challenges will be the ones we've had all along. Management wants to deliver a new version on April 1. Development is under the gun to release. Ops is under the gun to deploy. And you'll still have security experts saying: Let's make sure we didn't take any shortcuts writing the code; let's make sure we're tracing the right things. It would be nice if this conflict would go away, but I don't think it will. Not now, not ever. However, putting security and ops teams in the same group will help."

Different Budgets

Kirsten Newcomer, Director, Cloud and DevSecOps Strategy at Red Hat: "The purchasing decision and budgets for observability and security may be in different organizations."

Data Silos

Buddy Brewer, Chief Product Officer at Mezmo: "Currently, many organizations unintentionally lock data in silos that only certain teams can access, which often means DevOps and SecOps teams are either not getting the right data or implementing their individual solutions to get data from the same sources. While converging security and observability will make data significantly more actionable, organizations will be met with challenges with getting the data in the correct formats to be used by different tools they may need. In addition, they must make sure that they are adhering to regulations such as GDPR and CCPA and handle personal identifiable information (PII) properly."

Tool Silos

Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at Enterprise Management Associates (EMA) outlines several challenges to convergence. "First, the teams have separate tools with separate tool silos. Often, when these groups come together, they find the quality of the data collected by the other silo's tools are of poor quality. It's in a format that is useless to them, for instance. Also, there is no authoritative source of data. Both groups have their own data stores that represent the same truth about infrastructure and services, but the data disagrees with each other due to variations and data granularity, time stamping, etc."

"Neither group wants to give up control of tool strategy," McGillicuddy continues. "They're married to their individual tools. Which one will blink and give up their tool in favor of the other group's tool?"

Use the player or download the MP3 below to listen to EMA-APMdigest Podcast Episode 2 — Shamus McGillicuddy talks about Network Observability, the convergence of observability and security, and more.

Click here for a direct MP3 download of Episode 2 - Part 1

"We have a lot of work to do to make the tools work properly, so this is not an easy integration – largely because the observability tools were designed for observability. They were not designed for security purposes," adds Adam Hert, Director of Product at Riverbed.

Legacy Tools

Ajit Sancheti, GM, Falcon LogScale at CrowdStrike: "Legacy logging and event management tools may not provide the scale or the performance to ingest all data, which leads to ingest backlogs and sluggish search speed. Organizations should carefully evaluate logging products before attempting to collect all security and observability data in one tool."

Legacy Philosophies

Jam Leomi, Lead Security Engineer at Honeycomb: "The heart of the challenge in converging the two goes back to the culture shift we're seeing in security. A lot of today's practitioners are stuck in compliance practices or philosophies that are 30+ years old. As technology evolves, our security approach has to shift. This creates an opportunity to really connect security with the overall bottom line of the business instead of just as an afterthought. Observability as a tool and practice has the power to do a lot of the heavy lifting toward this goal, enabling a higher level of efficiency, security, and privacy."

Confidential Data

Kirsten Newcomer from Red Hat: "Some security data is not appropriate for sharing with all team members who need to consume observability data."

Security Experts are hard to find

Prashant Prahlad of Datadog: "Security experts are hard to find and take time to train within DevOps teams, so implementing DevSecOps is a long-term investment."

Knowledge Gap

Asaf Yigal, CTO of Logz.io: "Even for those that desire, or are prone to converge responsibilities, there's still a knowledge gap. Most often this is coming from the DevOps side, as in 'how do we take this important data and communicate effectively to security?' And the answer is: this is an emerging practice, so there's no wrong way, and we are working on the proverbial airplane whilst in flight!"

Despite all these challenges, Chaim Mazal, Chief Security Officer at Gigamon offers a positive outlook: "There are far fewer downsides to this convergence than there are advantages."

Go to: Exploring the Convergence of Observability and Security - Part 7: Advantages

Pete Goldin is Editor and Publisher of APMdigest

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...