The proliferation of data makes traditional IT visibility tools ineffective, unable to distinguish usual information from that which could constitute a threat. How to do it?
Modern IT systems are full of a constant stream of data that convey information about performance and security. This data is essential for IT professionals who need to ensure the uninterrupted operation of IT infrastructures.
The problem is that with such a volume of information, it is almost impossible to distinguish between regular information and information that could pose a threat. Visibility alone is not enough to differentiate valuable resources from useless information. Only increasingly sophisticated systems capable of interpreting and prioritizing information, not just collecting it and then intervening, are up to the task.
Unfortunately, today, most observability tools do not work this way. They generate alerts, log events and report anomalies. Even though technology is constantly evolving, people don’t always understand what’s happening and how to react.
Let’s take the example of a typical international company that uses a hybrid architecture. The latter includes mission-critical applications hosted by multiple cloud service providers, but also legacy systems on-premises. All of these systems are monitored by dozens of monitoring tools that generate thousands of alerts per day.
Some of them are false positives, others reveal minor regulatory violations, but somewhere amidst all this useless information lies a real threat to company security. And by the time it is detected, it is sometimes already too late.
Increased intelligence of observability functions
Additional tools and more visibility alone cannot fill the gaps. What is needed is a new, highly sophisticated level of observability that can function more like a human brain and filter out extraneous information, recognize essential information, and trigger the right interventions at the right time. We need an intelligent system capable of “thinking” for itself.
One reason we need it is that IT teams have tended to invest in separate tools that often lack context awareness capabilities. It is therefore up to the members of the IT team to establish connections and decide whether an alert is to be taken seriously, to identify the root cause and to initiate the appropriate corrective measures. In highly dynamic environments, these human-led assessments sometimes take time, which amplifies the risk.
On the other hand, an intelligent observability system would not only detect known problems. It would spot anomalies in real time through context-aware monitoring, then assess their severity and potential impact based on their technical and business relevance, as well as the risk they present.
Rather than treating each signal the same way, it would prioritize them based on urgency and risk, to help teams focus on what matters.
Most importantly, it would support automation functions to facilitate the application of common fixes and containment measures. Additionally, rather than scattering information across multiple disconnected views, such a system would consolidate data from on-premises and cloud environments into a single, cohesive view.
This type of system does more than just monitor networks and computer systems. He supervises all components and is ready to intervene, if necessary.
So when will this system be available?
The good news is that we are making progress. AI-driven observability is moving from ambition to implementation. Anomaly detection based on behavioral benchmarks is becoming increasingly accessible and helps teams distinguish real issues from false alerts. Alert correlation and intelligent anomaly escalation processes have improved and help reduce alert desensitization by routing relevant signals to the right people at the right time.
Some observability platforms (including SolarWinds) already consolidate monitoring, analysis, and response operations into more cohesive workflows. There remains the issue of integrating into various hybrid environments, but the pillars of intelligent observability are now in place.
Yet what is still missing is a type of system-wide intelligence that can replicate nuanced human decision-making. Most observability tools still rely on thresholds, templates or predefined rules. True context awareness, the ability to understand why an event is happening and determine what action to take, is still developing. But the trajectory is well defined.
Why it matters now
According to a recent SolarWinds AI and Observability report that focuses on the public sector, three-quarters of respondents said managing hybrid environments is complicated. Their main concerns include data protection, difficulty of integration and lack of visibility into systems.
The fact that observability tools are often in silos, one for the cloud, the other for on-premises systems with separate platforms for detection, logging and remediation, only further complicates the management of these complex environments.
Security further accentuates this unpredictability. In this report, more than half of IT professionals indicate that errors made internally are partly responsible for serious threats while 59% emphasize that increasingly sophisticated attacks come from the outside. With the advent of generative AI, these external threats are more scalable and targeted, increasing the pressure already on overworked IT teams.
That’s why the solution isn’t about adding more tools, it’s about reducing complexity, improving visibility, and taking smart action quickly. This is exactly what an observability system does, working more like a brain, because computer systems should do more than just observe. They must understand.
