Why traditional event correlation falls short in modern IT and how AIOps can help

02-Mar-2025 08:05 PM by Ramkumar Ramaswamy

Modern IT environments often span multiple cloud deployments that work in tandem with on-premises infrastructure, weaving across different microservices that share a dynamic workload. When IT is woven in complex ways, where every application or service works as well as the entire chain, it is vital for operations to observe comprehensively, without gaps or oversight.

A lot happens when a server request or an API call occurs. In observability speak, these requests are marked as "events". An event is any significant and relevant occurrence or change that is detectable anywhere in an IT system. Events can be error events (like disk exhaustion or connection timeouts), performance events (like latency or memory leaks), security events (failed logins or attacks), operational events (like deployments and backups), or informational events (like successful order placements).

A typical enterprise could generate hundreds of terabytes of data every day, and most of it is event logs from applications, servers, devices, networks, and databases. Collectively, this produces a large pool of data for observability. An observability solution should be adapted to modern IT, with the capability to store, retrieve, analyze and correlate all events. Event correlation is fundamental for understanding an IT system’s health, behavior, and performance, a role sacrosanct for DevOps and IT engineers.

Why traditional methods fall short

Eodern IT involves an expanding use of AI, enhancements from the DevOps culture, and traditional uses of containers, virtual machines, microservices-led architecture, multiple cloud, and others. Monitoring technology has not entirely caught up with contemporary IT needs due to various reasons. Traditional monitoring methods were often patched together in haste, as though they were an afterthought to development. Now, when things go wrong, it can be difficult for teams using traditional monitoring techniques to grasp the bigger picture and drill down to the root cause. These systems evolved on static, rule-based event correlation, and that is why, in the face of modern IT, they struggle to handle the scale, complexity, and speed of operations. For tech leaders, this implies a need to recognize these limitations and adopt a modern observability solutio n that can provide rich context and actionable insights by looking into all the layers of the infrastructure to correlate what happens, to fix issues faster, and better.

Traditional event correlation relies on predefined rules to connect incidents.

While traditional systems are simpler and easy-to-predict, they fall short of addressing the needs of modern IT due to the following reasons:

Inefficiencies and rigidity: As modern IT systems are constantly updated, scaled, and reconfigured, traditional monitoring systems are rule-based, and therefore, rigid. Attempts to update or adapt the rules to match the emerging changes are tedious and error-prone. For example, when a certain application behavior or anomaly lies outside the rule set, the traditional IT system will miss it and leave critical issues blissfully undetected.
Alert deluge, and yet, false positives: Static rules tend to trigger too many alerts, most of them low-priority or completely irrelevant–none of them qualified enough to mess with your sleep routines. But excessive notifications lead to alert fatigue that overwhelms IT teams, blocking their views and diverting their attention, which is crucial to spot genuine threats. Over time, excessive deluge alerts can numb a technician's ability to isolate the vital and important alerts from those of lesser priority, which further delays taking action on critical incidents.
Slow incident response: When IT environments shift and transform quickly, delays become costly. Traditional systems are unable to adapt to real-time needs and fail to detect anomalies that do not match their rule books. For example, when a network issue follows a problematic service interaction, the system may still fail to flag it until it veers to the edge of significant disruption.

AI-led event correlation: Scope, depth, and benefits

AIOps helps with better event correlation, due to its ability to process large data volumes in real time and use machine learning algorithms to uncover patterns, judge their relevance, mark the common factors and deliver actionable insights. Here are ways AIOps-led event correlation assists:

Real-time analysis and proactive insights: Unlike static rule-based systems, AI continuously learns from incoming data, identifying correlations and anomalies in real time. This capability supports proactive incident management, enabling teams to tackle issues before they worsen. For instance, AI might detect initial indicators of performance decline and associate these with recent changes, prompting immediate action.
Scalability for complex architectures: Today's IT landscapes frequently include multiple cloud services, microservices, and hybrid configurations. Traditional monitoring tools can falter when scaling across these diverse environments, but AI manages this complexity with ease. It processes and interprets data from various sources—logs, metrics, traces, etc.—to deliver a comprehensive overview of system health and performance.

Benefits of AI-led event correlation

AIOps is a driving force in fine-tuning security and performance alerts, improving troubleshooting, achieving compliance, and enhancing customer satisfaction.

Cuts noise, spots alerts better: AI filters alerts based on historical data, context, and severity, helping teams focus on high-priority issues. For example, when an alert aligns with past incidents that caused significant downtime, AIOps immediately flags them for attention, prioritizing them over the usual alert noise, substantially improving decision-making.
Reducing mean time to resolution: AIOps speeds up troubleshooting by pinpointing root causes quickly. When application latency spikes, for example, AI analyzes logs, metrics, and recent changes to determine whether the issue is tied to a configuration update, external traffic surge, or infrastructure problem. This precision minimizes downtime and accelerates resolution.
Ensuring SLA Compliance: Predictive analytics enable AI to detect potential SLA breaches before they occur. By analyzing trends and anomalies, AI alerts teams to emerging risks, such as resource exhaustion or service degradation, allowing them to take proactive measures to maintain compliance and ensure AIOps efforts meet SLAs.
Customer satisfaction: Thanks to faster remediation, customers can enjoy almost-uninterrupted services with acceptable service quality that promotes higher app rankings in app stores and improves customer satisfaction.

How a DevOps professional might monitor an application's performance

Initially, the team sets a threshold of two seconds for app response time to flag the status as "troubled" on its monitoring screens. While the app typically loads in about 300ms, it suddenly experiences a significant lag crossing the one-minute mark, which adversely affects user experience yet remains within the static limits set by the operations team.

This is where AIOps event correlation comes into play. By analyzing the application's performance history over 15 days or more, AIOps-powered monitoring identifies this as a problem, even though the status remains "normal," potentially going unnoticed and impacting the company's revenue and reputation. The issue can then be highlighted on a dashboard for the DevOps team's attention.

An observability platform can approach resolution in two ways:

Domain-aware approach: Here, a team-fed checklist of rules directs the troubleshooting process. For instance, the spike in response time might trigger checks on other components, like database queries and remote calls, down to the code level to pinpoint the infrastructure issue causing the delay.
AI-powered knowledge graph (KG) approach: This uses AI to explore the network of relationships connected to the current issue, correlating data to identify the root cause, and presenting actionable insights on the dashboard.

AI-led event correlation represents a significant advancement, dramatically reducing the mean time to response and facilitating quicker resolutions. It also opens avenues for automated remediation, significantly enhancing the customer experience. This makes AI-powered event correlation the next big leap in observability that is crucial for managing the complexity of modern IT infrastructure, and leading to improved operational efficiency, reduced downtime, and maintain service quality.

The future of IT management depends on intelligent systems that predict and prevent issues, not just react to them. For technology leaders, adopting AI-driven observability is essential for maintaining a competitive edge. ManageEngine Site24x7 helps organizations achieve comprehensive observability with AI-powered observability, empowering IT teams to succeed in today's complex environments. Try Site24x7 today .

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