Networking, Cybersecurity and Observability Are Converging

Harnessing the power of network-derived intelligence and insights is critical in detecting today's increasingly sophisticated security threats across hybrid and multi-cloud infrastructure, according to a new research study from IDC, conducted for Gigamon.

With 95% of organizations claiming to have experienced a ransomware attack in 2022, security remains top of mind for IT leaders regardless of their industry. According to the IDC White Paper, over 60% of respondents believe that today's observability solutions serve narrow requirements and fail to provide a complete view of current operating conditions.

To address today's rapidly evolving security requirements, enhancing traditional observability capabilities that rely on metrics, events, logs, and traces (MELT) with real-time network-derived intelligence and insights is essential to mitigate security risks across hybrid and multi-cloud infrastructure. Only with this deep observability can organizations find the greatest value from observability across both on-premises systems and cloud services, core and edge components, and cybersecurity functions.

"Networking, cybersecurity, and observability are becoming intertwined. IT organizations are looking to leverage an immutable source of truth and more collaborative management efforts to break down siloed technology approaches, position themselves for long-term success, and, ultimately, deliver the best possible business outcomes," said Mark Leary, Research Director with IDC. "Deep observability must be prioritized as IT organizations look to fully realize the transformational promise of a resilient and responsive digital infrastructure and continually maintain a strong security posture to meet today's digital business requirements."

Observability benefits include security, productivity and user experience

The top cited benefits of observability include security (34%), staff productivity (33%), and digital/user experience (25%). Observability also delivers a mix of both tactical (e.g., resolution, continuity, tracking) and strategic (e.g., experience, governance, innovation) benefits.

Deep observability will support automation

Over 75% of organizations use or plan to use deep observability solutions to support automation efforts in future years. Deep observability can enable a hierarchical platform-based approach in which detailed data and artificial intelligence (AI)/machine learning (ML)–driven analysis can produce a single source of truth, converge data and tools, and enable talent to deploy, operate, repair, and enhance digital infrastructures in a timely manner.

Cloud is priority

The market will see increased investments in cloud services over the next few years, with over half of respondents (51%) citing it as a priority. In fact, 72% of organizations strongly agree that cloud service intelligence should be leveraged to optimize costs and secure information. Cost from technical debt and the complexity of supporting multiple generations of infrastructure are some of the biggest barriers for organizations in achieving their digital infrastructure resiliency goals.

Network-derived intelligence can support Security

Network-derived intelligence can support adherence to SANS 20 Critical Security Controls, potentially eliminating 98% of possible attack vectors. Today, over 50% of respondents state that they actively share network intelligence across IT teams, and more than 60% of organizations are making progress in leveraging these insights in their security management practices.

"Over 90% of organizations operate in a hybrid and multi-cloud world, yet security blind spots remain a significant barrier for technology leaders looking to get the most out of their cloud investments," said Chaim Mazal, Chief Security Officer of Gigamon. "This research not only points to the critical role that deep observability plays in securing complex cloud environments but the necessary convergence of NetOps and SecOps teams in fortifying modern cybersecurity practices. "

Methodology: The findings are based on a survey, conducted by IDC, of over 900 global IT leaders across North America, APAC, and EMEA, which included a mix of major industries (financial, manufacturing, retail/wholesale, healthcare, transport/utilities, education, government, and professional services). All respondents held roles of manager or above, with key decision-making responsibilities for observability functions and solutions that span across IT operational domains, including networking, security, and cloud.

Hot Topics

The Latest

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

Networking, Cybersecurity and Observability Are Converging

Harnessing the power of network-derived intelligence and insights is critical in detecting today's increasingly sophisticated security threats across hybrid and multi-cloud infrastructure, according to a new research study from IDC, conducted for Gigamon.

With 95% of organizations claiming to have experienced a ransomware attack in 2022, security remains top of mind for IT leaders regardless of their industry. According to the IDC White Paper, over 60% of respondents believe that today's observability solutions serve narrow requirements and fail to provide a complete view of current operating conditions.

To address today's rapidly evolving security requirements, enhancing traditional observability capabilities that rely on metrics, events, logs, and traces (MELT) with real-time network-derived intelligence and insights is essential to mitigate security risks across hybrid and multi-cloud infrastructure. Only with this deep observability can organizations find the greatest value from observability across both on-premises systems and cloud services, core and edge components, and cybersecurity functions.

"Networking, cybersecurity, and observability are becoming intertwined. IT organizations are looking to leverage an immutable source of truth and more collaborative management efforts to break down siloed technology approaches, position themselves for long-term success, and, ultimately, deliver the best possible business outcomes," said Mark Leary, Research Director with IDC. "Deep observability must be prioritized as IT organizations look to fully realize the transformational promise of a resilient and responsive digital infrastructure and continually maintain a strong security posture to meet today's digital business requirements."

Observability benefits include security, productivity and user experience

The top cited benefits of observability include security (34%), staff productivity (33%), and digital/user experience (25%). Observability also delivers a mix of both tactical (e.g., resolution, continuity, tracking) and strategic (e.g., experience, governance, innovation) benefits.

Deep observability will support automation

Over 75% of organizations use or plan to use deep observability solutions to support automation efforts in future years. Deep observability can enable a hierarchical platform-based approach in which detailed data and artificial intelligence (AI)/machine learning (ML)–driven analysis can produce a single source of truth, converge data and tools, and enable talent to deploy, operate, repair, and enhance digital infrastructures in a timely manner.

Cloud is priority

The market will see increased investments in cloud services over the next few years, with over half of respondents (51%) citing it as a priority. In fact, 72% of organizations strongly agree that cloud service intelligence should be leveraged to optimize costs and secure information. Cost from technical debt and the complexity of supporting multiple generations of infrastructure are some of the biggest barriers for organizations in achieving their digital infrastructure resiliency goals.

Network-derived intelligence can support Security

Network-derived intelligence can support adherence to SANS 20 Critical Security Controls, potentially eliminating 98% of possible attack vectors. Today, over 50% of respondents state that they actively share network intelligence across IT teams, and more than 60% of organizations are making progress in leveraging these insights in their security management practices.

"Over 90% of organizations operate in a hybrid and multi-cloud world, yet security blind spots remain a significant barrier for technology leaders looking to get the most out of their cloud investments," said Chaim Mazal, Chief Security Officer of Gigamon. "This research not only points to the critical role that deep observability plays in securing complex cloud environments but the necessary convergence of NetOps and SecOps teams in fortifying modern cybersecurity practices. "

Methodology: The findings are based on a survey, conducted by IDC, of over 900 global IT leaders across North America, APAC, and EMEA, which included a mix of major industries (financial, manufacturing, retail/wholesale, healthcare, transport/utilities, education, government, and professional services). All respondents held roles of manager or above, with key decision-making responsibilities for observability functions and solutions that span across IT operational domains, including networking, security, and cloud.

Hot Topics

The Latest

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...