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Using APM for Security

There are quite a few architectures running around for Cloud and virtual environments, but except for a few, they seem to all be missing the ability to gain access to Application Performance Management (APM) data as a means to provide an early warning system for security issues.

Most security reference architectures rely on the old methods to get warnings about security issues such as use of a SIEM and a log analysis tool to interpret what is in the SIEM. However, there is a richer set of more immediate data that can help us with the problem of security notifications: APM Data.

APM Data provides a rich and different approach to security early warnings but the interpretation of the APM Data implies knowledge of the application that security professionals may not have. Yes, this is not a requirement as the security team and the applications team will be solving problems together that come up when there is an anomaly within any APM Data. The application team wants to know why there is an anomaly, perhaps a code path was taken unexpectedly, while the security team wants to insure that code path was not a hack attempt.

There are several ways to do this:

- Application and security professionals working together to determine if the APM Data shows a security issues or a code issue

- APM tools with built in mechanisms that could be used for security, such as a list of websites from which data comes into the system and to which data flows out of the system.

- APM tools that self learn the code path, so that when a new code path is used both security and application teams are notified

- APM tools that show both teams data about the code path when anomalies occur. Perhaps going so far as to highlight what was different

- APM Tools that show the exact process of events such as a database query to be investigated. Perhaps there was a SQL Injection within the query

APM tools have a rich set of data that could be used by security professionals. These tools know more about what is happening within an application than almost anyone else and could be helpful as a part of defense-in-depth. The smarter the APM tool, the more useful it becomes for security purposes.

Minimally, APM tools must contain the following abilities to be useful by security professionals:

- A way to see when external to the application resources were accessed, such as an external website.

- A way to see all database queries (even obfuscated if the APM solution is in the Cloud).

- A way to know when an anomaly has occurred, perhaps a different database query was made (possible SQL injection) or some normally unused code path was taken.

- A way to know when performance changes, perhaps activity is happening too fast (which could imply a DoS attack) or too slow (misconfigured or malware present).

In the end, however, it is all about determining when something anomalous has happened and a means of providing that data to the security team as well as the application team so that both work the problem side by side.

ABOUT Edward L. Halekty

Edward L. Halekty is Virtualization and Cloud Analyst, The Virtualization Practice LLC.

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Using APM for Security

There are quite a few architectures running around for Cloud and virtual environments, but except for a few, they seem to all be missing the ability to gain access to Application Performance Management (APM) data as a means to provide an early warning system for security issues.

Most security reference architectures rely on the old methods to get warnings about security issues such as use of a SIEM and a log analysis tool to interpret what is in the SIEM. However, there is a richer set of more immediate data that can help us with the problem of security notifications: APM Data.

APM Data provides a rich and different approach to security early warnings but the interpretation of the APM Data implies knowledge of the application that security professionals may not have. Yes, this is not a requirement as the security team and the applications team will be solving problems together that come up when there is an anomaly within any APM Data. The application team wants to know why there is an anomaly, perhaps a code path was taken unexpectedly, while the security team wants to insure that code path was not a hack attempt.

There are several ways to do this:

- Application and security professionals working together to determine if the APM Data shows a security issues or a code issue

- APM tools with built in mechanisms that could be used for security, such as a list of websites from which data comes into the system and to which data flows out of the system.

- APM tools that self learn the code path, so that when a new code path is used both security and application teams are notified

- APM tools that show both teams data about the code path when anomalies occur. Perhaps going so far as to highlight what was different

- APM Tools that show the exact process of events such as a database query to be investigated. Perhaps there was a SQL Injection within the query

APM tools have a rich set of data that could be used by security professionals. These tools know more about what is happening within an application than almost anyone else and could be helpful as a part of defense-in-depth. The smarter the APM tool, the more useful it becomes for security purposes.

Minimally, APM tools must contain the following abilities to be useful by security professionals:

- A way to see when external to the application resources were accessed, such as an external website.

- A way to see all database queries (even obfuscated if the APM solution is in the Cloud).

- A way to know when an anomaly has occurred, perhaps a different database query was made (possible SQL injection) or some normally unused code path was taken.

- A way to know when performance changes, perhaps activity is happening too fast (which could imply a DoS attack) or too slow (misconfigured or malware present).

In the end, however, it is all about determining when something anomalous has happened and a means of providing that data to the security team as well as the application team so that both work the problem side by side.

ABOUT Edward L. Halekty

Edward L. Halekty is Virtualization and Cloud Analyst, The Virtualization Practice LLC.

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.