How Does AI Handle Anomaly Detection?

Times change, and anomaly detection must also move quickly. Problem detection is critical in a log system and there are numerous ways to accomplish this task, some more effective than others. One current trend in analytics and problem detection is artificial intelligence (AI). While AI is used in all manner of industries and different technologies, its uses are particularly important in error detection.

Here we look at how AI solves problems in anomaly detection, and how an effective log analyzer uses AI for these critical tasks.

AI Determines Which Anomalies Count

It’s all very well turning up anomaly reports and problem searches, but if most of these alarms are false you waste considerable amounts of time and money. AI helps deliver less noise, so that there can be more effective problem-solving taking place. Not all problems are critical or even worth noting – but there has to be a decision made on which problems need actioning. AI is an effective way of figuring out whether an anomaly has the potential to impact systems. An AI detection engine automatically knows the difference between metrics and can therefore differentiate between when something will impact customers and when it will impact on business.

Helps Run Effective Automated Problem Detection

AI is also helpful because it fulfils a critical need in anomaly detection, that of being able to automate detection so that errors and problems are comprehensively and reliably flagged without manual searches. While log management tools have many systems in place to run an effective automated analysis, AI helps smooth the process and make it more efficient.

AI Works Well in Dynamic Environments

As systems evolve, there are fewer static systems and more dynamic services, cloud structures, and infrastructure. The many components, which are normally in perpetual motion, need analysis where the boundaries of “normal” are constantly refined and redefined. AI brings in continuous machine learning in order to automate dynamic processes and ensure that you can identify the anomalies that matter to you, in a constantly evolving system.

AI helps to run an efficient log management system as it dynamically focuses on fixing issues rather than finding them. It helps to define whether an anomaly needs to be actioned, and who it will affect. And one of the key differentiating features of AI within a log analysis system is the ability to change over time and get smarter the more it operates. This makes anomaly detection even more efficient over the long term, and AI helps to give ongoing insights into the trends that will affect systems in the future.

AI also works well within systems as it can help to fine-tune anomaly detection and help to deliver an overall better experience for more efficient and more effective error detection.

When searching for effective log analysis, look at systems that include functional AI to deliver better returns on investment in error detection and problem management.

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