Decoding AI-led event correlation for mastering modern IT management

Events are not always incidents

All events are not incidents; think about it. In IT observability , an event is any detectable occurrence or change within a system—such as a server request, API call, error log, or security breach. These events are a vital ingredient of IT observability—the ability to look into how a system functions from the outside. When critical events disrupt normal operations, they escalate into incidents that require immediate attention, preferably when the problem starts showing up and not as an afterthought. AI's role in IT observability is in its ability to help guess what happened and pinpoint emerging issues early by performing what is called event correlation.

Some observability challenges

With the above context in mind, here are some observability challenges IT teams deal with:

• Hybrid and multi-cloud deployments: Handling hybrid workloads spread across on-premises servers, as well as private and public cloud platforms, introduces fragmentation and blind spots.
• Rising costs : Downtime and inefficient troubleshooting can quickly overwhelm IT budgets.
• Data deluge and diversity: Every day, a typical IT organization generates terabytes of observability data in the form of metrics, events, logs, and traces, and it must sift through the noise to gain actionable insights.
• Less time to resolve: User expectations keep rising to impossible levels, and at the same time, IT operations teams' window for resolving incidents shrinks, leaving little room for manual analysis.
• Tool sprawl : Multiple disjointed tools create silos, making it harder to get a unified view of system health.

A balanced approach that rests on AI's capabilities will help IT leaders adopt a smarter approach to address the above challenges.

Challenges in tracking and responding to IT events

Modern IT event tracking is more than data collection and goes deeper into understanding the relationships and patterns within the dataset. It asks these pertinent questions: How does a database query timeout connect to a network bottleneck? Based on historical and emerging patterns, when there is a minor performance dip, what are the chances that it could snowball into a major outage?

Traditional monitoring methods rely on rigid, static rules that are prone to oversight. They do not adapt to evolving norms and could easily mislead teams into wasting time analyzing wrong or benign signals. This could prevent them from responding to real situations in time, which would make downtime costlier. What is needed is a solution that not only tracks events but also interprets them intelligently to help respond proactively and decisively.

Understanding event correlation

Event correlation analyzes the hidden relationships between disparate events to diagnose system health holistically, like piecing together a puzzle. In this pursuit, though individual events may appear innocuous, when they are linked, they reveal the bigger picture.

AI takes this concept to the next level. Algorithms can now analyze large troves of historical and real-time data in tandem to uncover hidden patterns and anomalies and correlate events to predict incidents anywhere in your IT stack. For example, AI can correlate a surge in CPU usage with a recent code push by using advanced techniques like clustering and Bayesian networks. Correlation helps teams roll back the problematic code and restore the business application faster. This is how AIOps transforms reactive monitoring into proactive observability.

AIOps in event correlation

AIOps uses ML algorithms to train on historical observability data, typically spanning days to months, to create a holistic baseline of what is considered normal behavior. Armed with this understanding, all new data is continuously compared, benchmarked, and judged against legitimate and worrisome deviations from the baseline. AIOps enriches this further with contextual information, such as timestamps, dependencies, and past incidents, alerting teams to perform corrective actions.

1. Collect data centrally: Aggregate logs, metrics, traces, and events from all sources.
2. Identify and eliminate patterns: Identify recurring trends and deviations using AI-led correlation techniques like time series alignment.
3. Analyze in context: Link related events across infrastructure layers by mapping temporal patterns and causal dependencies.
4. Perform root cause analysis: Identify primary failure points by correlating events and prioritizing issues based on severity and impact.

This intelligent approach is proactive and helps avoid firefighting situations that halt productivity and dent employee morale.

Advantages of AI-led event correlation

Here are five benefits of AI-driven event correlation that go further than what traditional monitoring could deliver to reduce downtime and increase customer satisfaction:

1. Efficiency leap: AI can automate tough and repetitive tasks like log parsing and anomaly detection to free up human resources.
2. Noise reduction: AI is better than humans at sifting through a deluge of alerts to filter out what is irrelevant and help you focus only on high-priority issues.
3. Eliminates alert fatigue : With intelligent alerting, AI guards your IT teams from being overwhelmed by false positives or low-value notifications.
4. Faster resolution: AI cuts down mean time to resolution by providing precise insights into root causes.
5. Proactive insights: It predicts potential issues before they escalate and interrupt operations.

How Site24x7's AIOps event correlation can help

Consider a global e-commerce platform suffering intermittent slowdowns, especially during peak hours. Using traditional tools, the IT team struggles to identify whether the issue stems from overloaded servers, misconfigured APIs, or third-party integrations.