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

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

The experts have all agreed that security teams can gain great benefits from utilizing observability data. But does this mean security and observability tools should be integrated, or even combined?

Chaim Mazal, Chief Security Officer at Gigamon says the answer to this question is a resounding yes.

"Observability tools are powerful at aiding organizations in identifying security anomalies and pinpointing performance bottlenecks at the application layer. Logging provides foundational visibility into the applications running across their hybrid cloud infrastructure. But, as threat actors apply increasingly sophisticated techniques to breach an organization's technology environment, network-derived intelligence is vital to detecting lateral movement should a threat actor successfully gain access. If successful, threat actors can move across an organization undetected seeking to exploit proprietary or confidential information for financial gain. It's only by integrating logging with network-derived intelligence that IT organizations gain deep observability across their hybrid and multi-cloud infrastructure to detect previously unseen threats, deliver defense in depth, and complete performance management."

"Security and observability tools should absolutely be combined," says Prashant Prahlad, VP of Cloud Security Products at Datadog. "Traditional security solutions are targeted solely at security professionals. But, while security pros are responsible for finding vulnerabilities, misconfigurations and risks, developers are the ones responsible for fixing them. This is especially true when it comes to cloud security as most of the remediation requires working with a DevOps team."

"For example, security can't change the configuration of a s3 bucket without the risk of breaking something in production, which is why it is critical to have the DevOps and security teams aligned," Prahlad continues. "Because traditional solutions are aimed at security pros — who traditionally managed network security — they don't provide the shared context that organizations need to fix issues quickly and efficiently. A unified platform for observability and security is needed so that developers can work directly with security pros to visualize how threats and vulnerabilities are impacting their cloud environments and prioritize fixes faster. This approach breaks down silos between DevOps and security teams and creates the shared context they need to secure cloud environments."

However, convergence is difficult to prescribe, cautions Asaf Yigal, CTO of Logz.io. "Literally every organization is going to require a unique approach based on its specific makeup, whether this is a large or mature org with a lot of people given responsibility for dev, ops, security or even platform engineering. The platforms and tooling need to match the people and process, or evolve with it."

"At the same time, we know for sure that there is a huge benefit in bringing together the relevant data, either to be actioned centrally, say in a smaller shop with only a few people responsible for DevSecOps, or to be communicated across teams in a larger org with multiple groups spanning the entire landscape."

"There's also the huge benefit of tapping into a common data set," Yigal adds, "namely logs, and using a shared platform; this is for a lot of reasons, from using a common language for querying engines, etc., to having fewer vendors to manage. This is why nearly every major observability vendor also markets a SIEM — it just makes a lot of sense."

Adam Hert, Director of Product at Riverbed agrees that tools should be integrated, but says, "Security and observability tools don't need to be combined. Some teams are trying to do this, but it does not make sense for organizations to do so, largely because you have two teams focused on very different goals. Security teams are tracking down threats, while observability teams are focused on making the enterprise more efficient and effective. Observability and security tools don't need to be combined, but they need to be able to integrate so that security tools can ask questions on the observability data."

Convergence Saves Money

"On the one hand, there's an argument to be made that security and observability tools should not be combined as most traditional monitoring and logging tools get bogged down by the strict retention requirements that are required by security tools for regulatory and compliance purposes of their products," says Jam Leomi, Lead Security Engineer at Honeycomb. "Applying that type of forensic-level, unsampled logging to observability tools would both be costly in terms of expense and speed, but also very inefficient."

"However, combining security and observability tools does have some functionality as it would cut down on costs drastically while creating an open field for collaboration between security, engineering, and the business to address incident response and the overall security posture assessment — generally, because there's a lot of natural crossover between the goals and initiatives for security and observability teams," Leomi continues. "For example, SOC2 controls require teams to keep up with performance metrics which observability platforms can offer fresh insights into data, even without having the granularity of each forensic row."

Colin Fallwell, Field CTO of Sumo Logic agrees that any time teams can unify data and interfaces for managing observability and security, it's a win, both in reducing the cost of ownership as well as ROI in uniformity and standards. "DevOps and SecOps need the same data, so why have two collection pipelines, for separate tools, capturing the same telemetric data? It really doesn't make sense. This redundancy is expensive and unnecessary."

"Additionally, there's a shortage of specialized security talent with the skillset needed to shift security left," Leomi from Honeycomb informs. "Organizations are under increasing pressure to reduce spend without sacrificing ability, so naturally, they look for tools that can perform multiple functions like the ability to observe application performance while also being able to identify security vulnerabilities."

"Further exacerbating this trend is the scarcity of security talent needed to drive and meet security initiatives," Leomi adds. "This has driven organizations to rely on what they have, which is often product and platform engineering departments that are already using a tool for observability and one that can provide a good enough starting point for security."

Go to: Exploring the Convergence of Observability and Security - Part 4: Dashboards

Pete Goldin is Editor and Publisher of APMdigest

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

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

The experts have all agreed that security teams can gain great benefits from utilizing observability data. But does this mean security and observability tools should be integrated, or even combined?

Chaim Mazal, Chief Security Officer at Gigamon says the answer to this question is a resounding yes.

"Observability tools are powerful at aiding organizations in identifying security anomalies and pinpointing performance bottlenecks at the application layer. Logging provides foundational visibility into the applications running across their hybrid cloud infrastructure. But, as threat actors apply increasingly sophisticated techniques to breach an organization's technology environment, network-derived intelligence is vital to detecting lateral movement should a threat actor successfully gain access. If successful, threat actors can move across an organization undetected seeking to exploit proprietary or confidential information for financial gain. It's only by integrating logging with network-derived intelligence that IT organizations gain deep observability across their hybrid and multi-cloud infrastructure to detect previously unseen threats, deliver defense in depth, and complete performance management."

"Security and observability tools should absolutely be combined," says Prashant Prahlad, VP of Cloud Security Products at Datadog. "Traditional security solutions are targeted solely at security professionals. But, while security pros are responsible for finding vulnerabilities, misconfigurations and risks, developers are the ones responsible for fixing them. This is especially true when it comes to cloud security as most of the remediation requires working with a DevOps team."

"For example, security can't change the configuration of a s3 bucket without the risk of breaking something in production, which is why it is critical to have the DevOps and security teams aligned," Prahlad continues. "Because traditional solutions are aimed at security pros — who traditionally managed network security — they don't provide the shared context that organizations need to fix issues quickly and efficiently. A unified platform for observability and security is needed so that developers can work directly with security pros to visualize how threats and vulnerabilities are impacting their cloud environments and prioritize fixes faster. This approach breaks down silos between DevOps and security teams and creates the shared context they need to secure cloud environments."

However, convergence is difficult to prescribe, cautions Asaf Yigal, CTO of Logz.io. "Literally every organization is going to require a unique approach based on its specific makeup, whether this is a large or mature org with a lot of people given responsibility for dev, ops, security or even platform engineering. The platforms and tooling need to match the people and process, or evolve with it."

"At the same time, we know for sure that there is a huge benefit in bringing together the relevant data, either to be actioned centrally, say in a smaller shop with only a few people responsible for DevSecOps, or to be communicated across teams in a larger org with multiple groups spanning the entire landscape."

"There's also the huge benefit of tapping into a common data set," Yigal adds, "namely logs, and using a shared platform; this is for a lot of reasons, from using a common language for querying engines, etc., to having fewer vendors to manage. This is why nearly every major observability vendor also markets a SIEM — it just makes a lot of sense."

Adam Hert, Director of Product at Riverbed agrees that tools should be integrated, but says, "Security and observability tools don't need to be combined. Some teams are trying to do this, but it does not make sense for organizations to do so, largely because you have two teams focused on very different goals. Security teams are tracking down threats, while observability teams are focused on making the enterprise more efficient and effective. Observability and security tools don't need to be combined, but they need to be able to integrate so that security tools can ask questions on the observability data."

Convergence Saves Money

"On the one hand, there's an argument to be made that security and observability tools should not be combined as most traditional monitoring and logging tools get bogged down by the strict retention requirements that are required by security tools for regulatory and compliance purposes of their products," says Jam Leomi, Lead Security Engineer at Honeycomb. "Applying that type of forensic-level, unsampled logging to observability tools would both be costly in terms of expense and speed, but also very inefficient."

"However, combining security and observability tools does have some functionality as it would cut down on costs drastically while creating an open field for collaboration between security, engineering, and the business to address incident response and the overall security posture assessment — generally, because there's a lot of natural crossover between the goals and initiatives for security and observability teams," Leomi continues. "For example, SOC2 controls require teams to keep up with performance metrics which observability platforms can offer fresh insights into data, even without having the granularity of each forensic row."

Colin Fallwell, Field CTO of Sumo Logic agrees that any time teams can unify data and interfaces for managing observability and security, it's a win, both in reducing the cost of ownership as well as ROI in uniformity and standards. "DevOps and SecOps need the same data, so why have two collection pipelines, for separate tools, capturing the same telemetric data? It really doesn't make sense. This redundancy is expensive and unnecessary."

"Additionally, there's a shortage of specialized security talent with the skillset needed to shift security left," Leomi from Honeycomb informs. "Organizations are under increasing pressure to reduce spend without sacrificing ability, so naturally, they look for tools that can perform multiple functions like the ability to observe application performance while also being able to identify security vulnerabilities."

"Further exacerbating this trend is the scarcity of security talent needed to drive and meet security initiatives," Leomi adds. "This has driven organizations to rely on what they have, which is often product and platform engineering departments that are already using a tool for observability and one that can provide a good enough starting point for security."

Go to: Exploring the Convergence of Observability and Security - Part 4: Dashboards

Pete Goldin is Editor and Publisher of APMdigest

The Latest

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...