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5 Ways to Use APM for Post-Event Security Forensics

Brad Reinboldt

Most security experts agree that the rapidly changing nature of malware, hack attacks and government espionage practically guarantees your IT infrastructure will be compromised. According to the 2014 Cost of Data Breach Study conducted by the Ponemon Institute, the average detection, escalation and notification costs for a breach is approximately $1 million. Post-incident costs averaged $1.6 million.

Once an attacker is within the network, it can be very difficult to identify and eliminate the threat without deep-packet inspection. The right Application Performance Management (APM) solution that includes network forensics can help IT operations deliver superior performance for users, and when incorporated into your IT security initiatives, deep packet inspection can provide an extra level of support to existing antivirus software, Intrusion Detection System (IDS) and Data Loss Prevention (DLP) solutions. The ability to capture and store all activity that traverses your IT infrastructure acts like a 24/7 security camera that enables your APM tool to serve as a backstop to your business’ IT security efforts if other lines of defense fail.

To use APM solutions for security forensics for post-event analysis, you must have a network retrospective analyzer that has at least the following capabilities:

■ High-speed (10 Gb and 40 Gb) data center traffic capture

■ Expert analytics of network activity with deep packet inspection

■ Filtering using Snort or custom user defined rules

■ Event replay and session reconstruction

■ Capacity to store massive amounts of traffic data (we’re potentially talking petabytes) for post-event analysis

Like utilizing video footage from a surveillance camera, captured packets and analysis of network conversations can be retained and looked at retrospectively to detect, clean up and provide detailed information of a breach. This back-in-time analysis can be especially important if the threat comes from within, such as a disgruntled employee within a company firewall. It also allows companies to determine exactly what data was compromised and help in future prevention.

Below are five ways to use network monitoring and analysis to investigate breaches:

1. Identify changes in overall network traffic behavior, such as applications slowing down that could be a sign of an active security breach.

2. Detect unusual individual user’s account activity; off-hour usage, large data transfers, or attempts to access unauthorized systems or services — actions often associated with disgruntled employees or a hacked account.

3. Watch for high-volume network traffic at unusual times, it could be a rogue user in the process of taking sensitive data or stealing company IP.

4. View packet capture of network conversations to determine how the breach occurred and develop strategies to eliminate future threats by strengthening the primary IT security.

5. Discover what infrastructure, services, and data were exposed to aid in resolution, notification, and regulatory compliance.

By incorporating retrospective network analysis, companies can use their network monitoring as a back stop to IDS and DLP solutions, and accelerate detection and resolution.

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5 Ways to Use APM for Post-Event Security Forensics

Brad Reinboldt

Most security experts agree that the rapidly changing nature of malware, hack attacks and government espionage practically guarantees your IT infrastructure will be compromised. According to the 2014 Cost of Data Breach Study conducted by the Ponemon Institute, the average detection, escalation and notification costs for a breach is approximately $1 million. Post-incident costs averaged $1.6 million.

Once an attacker is within the network, it can be very difficult to identify and eliminate the threat without deep-packet inspection. The right Application Performance Management (APM) solution that includes network forensics can help IT operations deliver superior performance for users, and when incorporated into your IT security initiatives, deep packet inspection can provide an extra level of support to existing antivirus software, Intrusion Detection System (IDS) and Data Loss Prevention (DLP) solutions. The ability to capture and store all activity that traverses your IT infrastructure acts like a 24/7 security camera that enables your APM tool to serve as a backstop to your business’ IT security efforts if other lines of defense fail.

To use APM solutions for security forensics for post-event analysis, you must have a network retrospective analyzer that has at least the following capabilities:

■ High-speed (10 Gb and 40 Gb) data center traffic capture

■ Expert analytics of network activity with deep packet inspection

■ Filtering using Snort or custom user defined rules

■ Event replay and session reconstruction

■ Capacity to store massive amounts of traffic data (we’re potentially talking petabytes) for post-event analysis

Like utilizing video footage from a surveillance camera, captured packets and analysis of network conversations can be retained and looked at retrospectively to detect, clean up and provide detailed information of a breach. This back-in-time analysis can be especially important if the threat comes from within, such as a disgruntled employee within a company firewall. It also allows companies to determine exactly what data was compromised and help in future prevention.

Below are five ways to use network monitoring and analysis to investigate breaches:

1. Identify changes in overall network traffic behavior, such as applications slowing down that could be a sign of an active security breach.

2. Detect unusual individual user’s account activity; off-hour usage, large data transfers, or attempts to access unauthorized systems or services — actions often associated with disgruntled employees or a hacked account.

3. Watch for high-volume network traffic at unusual times, it could be a rogue user in the process of taking sensitive data or stealing company IP.

4. View packet capture of network conversations to determine how the breach occurred and develop strategies to eliminate future threats by strengthening the primary IT security.

5. Discover what infrastructure, services, and data were exposed to aid in resolution, notification, and regulatory compliance.

By incorporating retrospective network analysis, companies can use their network monitoring as a back stop to IDS and DLP solutions, and accelerate detection and resolution.

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The Latest

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...