A new way to perform alert triage in complex IT systems

Artificial intelligence is transforming the company landscape, allowing all departments, from sales to finance, to gain accurate insights both in real-time and in the future.

We’re seeing the next generation of IT analytics, including automation, visualization, correlation, and dependency mapping, when it comes to AI in IT operations.

Surprisingly, IT businesses have yet to fully exploit all of AI and machine learning’s capabilities for their processes.

Main benefits that AI is aimed to bring

Making all processes visible

An AI system can dig down to the atomic level of data, allowing IT personnel to examine how each process works and detect potential bottlenecks or other sources of outages. It is easier to identify possible hazards and mitigate them before they become real problems when your firm has a clear picture of the system.

Acting on a data-based decision process

It’s best to base IT operations adjustments, like any other company choice, on data and facts rather than hunches. An AI system can access historical data more efficiently, filter out noise, and do automatic root cause investigation. Having this knowledge allows you to make better forecasts and solve problems faster, as well as anticipate potential problems in the future.

Automating responses and tasks

Even if the system detects potential dangers and enhancements, it is still wasteful and adds to alert fatigue if the final decision must be approved by a member of the IT team. The capacity to automate responses and configurations to fit the system’s condition is a significant improvement of adopting an AI platform over prior generations of IT operations management systems.

Better IT system protection and security

The patterns deviate from normal operations when hackers attempt to assault a system or when some sub-systems generate failures. An artificial intelligence platform can recognize improper behavior and take the required steps to prevent it.

The first stage of full AI integration into IT systems: Alert Triage

In today’s hectic corporate contexts, NOC and SOC teams must either investigate each warning individually, start ignoring some, outsource, or look for automated solutions to assist them to prioritize and sift the influx of alarms.

The problem is that most warnings are either insignificant or can be resolved with a simple “repair” that does not involve human interaction. The manual investigation, on the other hand, remains a time-consuming option that puts the engineers under a lot of stress in the absence of an automatic triage system.

Training AI-based tools to solve this is a viable solution that is now gaining traction. This alternative is best defined as AI-powered process automation in Alert Triage (for more information, see Siscale’s Arcanna.ai). The goal is to only send a few key warnings to the human team, avoid false positives, and ensure that no important alert goes unnoticed.

Alert triage in a few words

When a breach occurs, alert triage should classify it as a high-risk, high-priority scenario and contact the NOC or SOC teams as soon as possible. The triage system determines whether an alarm should be ignored or escalated when it is generated.

The signature of the warning is compared to past data from historical logs, previous breach situations, and other complex know-how in this procedure. Each assessment considers the assets that have been harmed, the attack’s aim and source, the attack’s progression, and so on.

When detecting threats, procedures must first identify the possible issue by assessing the entrance point, the harm was already done, and the problem’s magnitude. The next stage is to put the danger into perspective, and the final step is to contain the attack, neutralize it, and restore system status while documenting all of the steps performed.

A new concept: AI aided alert triaging

An AI-based technology that uses big data to learn about your firm and translate that into relevant attack responses can customize alert responses to your enterprise. The technology saves time and money by applying the decision matrix automatically once the training and calibration are completed.

By integrating root cause analysis, artificial intelligence (AI) assists support teams in eliminating false positives. Instead of only fixing the outcomes, this method aims to uncover the actual cause of the alert. The program works its way upstream through a series of options, computing potential correlations at each step. Causality is also taken into account, and each occurrence is compared to comparable patterns in a database.

Siscale’s Arcanna.ai triage mechanism, for example, takes data from the system in real-time and escalates alarms that check all the boxes for a disaster waiting to happen. What’s more, Siscale’s Deep Learning model learns not only from the data it’s studying but also from the answers provided by human specialists, continually adapting to the system.

The Importance of better alert triaging in CyberSecurity

To summarize, without intelligent technologies that automate the alert triage process, an organization’s cybersecurity system is incomplete. Due to the volume and pace of incoming data, a human inspection of each alert is not practical.

A ticket-based security model is practical after carefully filtering out the noise and keeping only those signals that are serious dangers to the system. Otherwise, you risk having a security team that is overworked and disengaged.