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Urgency Grows for Observability and Security Convergence

Organizations find increasing difficulty in maintaining software reliability and security as the demand for continuous release cycles and the rising complexity of cloud-native environments create more risk for undetected defects and vulnerabilities to escape into production, according to the 2023 Global CIO Report, Observability and Security Convergence: Enabling Faster, More Secure Innovation in the Cloud, from Dynatrace.

CIOs and senior DevOps managers are looking to DevSecOps processes, the convergence of observability and security, and the increased use of AI and automation to balance accelerated innovation with reliability and security.

The research reveals the following:

■ 90% of organizations say digital transformation has accelerated in the past 12 months.

■ 78% of organizations deploy software updates into production every 12 hours or less, and 54% say they do so at least once every two hours.

■ DevOps teams spend nearly a third (31%) of their time on manual tasks involving detecting code quality issues and vulnerabilities, reducing the time spent on innovation.

■ 55% of organizations make tradeoffs between quality, security, and user experience to meet the need for rapid transformation.

■ 88% of CIOs say the convergence of observability and security practices will be critical to building a DevSecOps culture, and 90% say increasing the use of AIOps will be key to scaling up these practices.

"It's difficult for teams to accelerate the pace of innovation while also maintaining the highest quality and security standards," said Bernd Greifeneder, Founder and CTO at Dynatrace. "More frequent software deployments, combined with complex cloud-native architectures, make it easier for errors and vulnerabilities to escape into production where they impact customer experience and create risk. There simply aren't enough hours in the day for teams to test code as thoroughly as when they had only a single monthly deployment, but there's no margin for error in today's ultra-competitive, always-on economy. Something has to change."


Additional findings from the survey include:

■ Organizations plan to increase their spending on automation across development, security, and operations by 35% by 2024, as they invest more in continuously testing software quality (54%) and security (49%) in production, automatic vulnerability detection and blocking (41%), and automating release validation (35%).

■ 70% of CIOs say they need to improve their trust in the accuracy of AI's decisions before they can automate more of the CI/CD pipeline.

■ 94% of CIOs say extending a DevSecOps culture to more teams is key to accelerating digital transformation and driving faster, more secure software releases.

"Organizations know that manual approaches aren't scalable," continued Greifeneder. "Teams can't afford to waste time and effort chasing false positives, searching for vulnerabilities whenever a new threat alert appears, or conducting forensics to understand whether data has been compromised. They need to work together to drive faster, more secure innovation. Automation and modern delivery practices such as DevSecOps are key to this, but teams need to trust that their AI is reaching the right conclusions about the impact of a particular vulnerability. To accomplish this, organizations require a unified platform that can converge observability and security data to eliminate the silos between teams. By bringing their data together and retaining its context, DevOps and security teams can unlock the insights they need through causal AI. This enables them to harness intelligent automation to rapidly deliver high-performing and secure applications that delight their users."

Methdology: The report is based on a global survey of 1,300 CIOs and senior IT practitioners involved in DevOps management in large organizations with more than 1,000 employees, conducted by Coleman Parkes and commissioned by Dynatrace. The sample included 200 respondents in the US, 100 in Latin America, 600 in Europe, 150 in the Middle East, and 250 in Asia Pacific.

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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 ...

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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 ...

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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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Urgency Grows for Observability and Security Convergence

Organizations find increasing difficulty in maintaining software reliability and security as the demand for continuous release cycles and the rising complexity of cloud-native environments create more risk for undetected defects and vulnerabilities to escape into production, according to the 2023 Global CIO Report, Observability and Security Convergence: Enabling Faster, More Secure Innovation in the Cloud, from Dynatrace.

CIOs and senior DevOps managers are looking to DevSecOps processes, the convergence of observability and security, and the increased use of AI and automation to balance accelerated innovation with reliability and security.

The research reveals the following:

■ 90% of organizations say digital transformation has accelerated in the past 12 months.

■ 78% of organizations deploy software updates into production every 12 hours or less, and 54% say they do so at least once every two hours.

■ DevOps teams spend nearly a third (31%) of their time on manual tasks involving detecting code quality issues and vulnerabilities, reducing the time spent on innovation.

■ 55% of organizations make tradeoffs between quality, security, and user experience to meet the need for rapid transformation.

■ 88% of CIOs say the convergence of observability and security practices will be critical to building a DevSecOps culture, and 90% say increasing the use of AIOps will be key to scaling up these practices.

"It's difficult for teams to accelerate the pace of innovation while also maintaining the highest quality and security standards," said Bernd Greifeneder, Founder and CTO at Dynatrace. "More frequent software deployments, combined with complex cloud-native architectures, make it easier for errors and vulnerabilities to escape into production where they impact customer experience and create risk. There simply aren't enough hours in the day for teams to test code as thoroughly as when they had only a single monthly deployment, but there's no margin for error in today's ultra-competitive, always-on economy. Something has to change."


Additional findings from the survey include:

■ Organizations plan to increase their spending on automation across development, security, and operations by 35% by 2024, as they invest more in continuously testing software quality (54%) and security (49%) in production, automatic vulnerability detection and blocking (41%), and automating release validation (35%).

■ 70% of CIOs say they need to improve their trust in the accuracy of AI's decisions before they can automate more of the CI/CD pipeline.

■ 94% of CIOs say extending a DevSecOps culture to more teams is key to accelerating digital transformation and driving faster, more secure software releases.

"Organizations know that manual approaches aren't scalable," continued Greifeneder. "Teams can't afford to waste time and effort chasing false positives, searching for vulnerabilities whenever a new threat alert appears, or conducting forensics to understand whether data has been compromised. They need to work together to drive faster, more secure innovation. Automation and modern delivery practices such as DevSecOps are key to this, but teams need to trust that their AI is reaching the right conclusions about the impact of a particular vulnerability. To accomplish this, organizations require a unified platform that can converge observability and security data to eliminate the silos between teams. By bringing their data together and retaining its context, DevOps and security teams can unlock the insights they need through causal AI. This enables them to harness intelligent automation to rapidly deliver high-performing and secure applications that delight their users."

Methdology: The report is based on a global survey of 1,300 CIOs and senior IT practitioners involved in DevOps management in large organizations with more than 1,000 employees, conducted by Coleman Parkes and commissioned by Dynatrace. The sample included 200 respondents in the US, 100 in Latin America, 600 in Europe, 150 in the Middle East, and 250 in Asia Pacific.

The Latest

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...