Jerônimo do Valle
Recently, it has been revealed that a considerable number of companies are already using AIOps to help predict problems and understand their causes. This rapid adoption is based on the ability to automate repetitive operations - sometimes trivial in appearance - where the main components are advanced analytics and machine learning.
It is common, due to the constant increase in the volume of data to be processed, that errors and unwanted results inevitably happen, however, with AIOps acting in the detection of anomalies, IT teams are able to implement the cause analysis practically in real-time and, thus, avoid delays and interruptions in services.
When it comes to managing the data storage process, resources can also be controlled with AIOps, automating routine tasks such as reconfiguration and recalibration. Predictive analytics can proactively install new volumes so that space can be made available as needed.
Finally, talking about security, the advantage is overwhelming. The application of AI allows data breaches and infractions to be revealed. Machine learning algorithms can be employed to detect harmful activity by collecting and integrating internal logs, application logs, network and event logs, as well as malicious IPs and domain information and third-party sources. As AI-based algorithms become more powerful, companies can use the technology to uncover potential threats hidden in any part of their IT infrastructure.
In a brief analysis of the situation, it is concluded that the entire digital landscape of the planet is, once again, about to change. It's really time to embrace AI as a disruptive force in forecasting problems, reducing costs, managing resources, improving customer service and, not least, refocusing IT teams on developing leading-edge solutions .