Smart observability is the key tool for hybrid IT management.

Current IT infrastructures are flooded with data that provide information about system performance and security. This information is essential for IT professionals seeking to keep their infrastructures operational. However, the abundance of data presents a challenge, as it becomes difficult to distinguish between what is routine and what could represent a threat. Separating the relevant from the trivial requires more than visibility; it necessitates sophisticated systems that can interpret, prioritize, and act, rather than just collect information.

Unfortunately, most of the observability tools available on the market do not operate this way. While they generate alerts, log events, and highlight anomalies, they do not always understand what is happening or how to respond to it. An example is a global company operating with a hybrid architecture, where its critical applications may be distributed across multiple cloud providers and also depend on legacy on-premises systems. These environments are monitored by various tools that generate thousands of alerts daily, many of which are false positives or minor issues. However, hidden within this cacophony could be a real security incident, and detecting it in a timely manner can be a complicated task.

It is crucial to have a more advanced form of observability, similar to the functioning of the human brain, capable of filtering the noise, recognizing what truly matters, and triggering the appropriate response at the right moment. This intelligent observability would not only monitor known issues but also detect anomalies in real time through contextual monitoring, assessing the severity and potential impact both technically and business-wise, thereby prioritizing alerts according to their urgency and risk.

Moreover, this intelligent system would facilitate the automation of routine fixes or containment measures, integrating data from both on-premises and cloud environments into a coherent view. An efficient observability system not only monitors but also has complete control and is prepared to act when necessary.

Fortunately, advancements are being made toward the implementation of AI-driven observability. Behavior-based anomaly detection is becoming more accessible, enabling teams to differentiate between real issues and false alarms. Some observability platforms, such as those developed by SolarWinds, are already integrating monitoring, analysis, and response into more cohesive workflows, although the challenge of integration in hybrid environments remains.

However, a complete system intelligence that can replicate the nuance of human decision-making is still needed, as many observability tools still rely on thresholds and pre-defined templates. True contextual awareness, which allows for an understanding of why something is happening and what action to take next, is still under development, although the direction is clear.

Regarding its current relevance, a recent report on artificial intelligence and observability revealed that three-quarters of respondents find hybrid environments difficult to manage, highlighting concerns about data protection, integration complexity, and lack of visibility in systems. This complexity is exacerbated by the trend of using isolated tools that operate separately.

Furthermore, security adds to the unpredictability, with more than half of IT professionals noting that internal errors contribute to serious threats, and 59% warning about increasingly sophisticated attacks from external actors. The emergence of generative AI has made these threats more scalable and targeted, increasing pressure on IT teams.

Therefore, the focus should not be on adding more tools but on reducing complexity, improving visibility, and acting intelligently and quickly. An observability system that functions similarly to a brain meets this need, as IT systems must do more than just observe; they must also understand.