Managing security is an increasingly complicated task for a number of reason. First, networks are expanding rapidly, and many organizations have found that their visibility across the network has been significantly reduced. Siloed security tools and isolated network development and security projects have resulted in vendor sprawl, which means more management consoles to track and more data that isn’t being correlated quickly enough to detect fast-moving threats.
Filling the Skills Gap with Machine Learning and Artificial Intelligence
The other issue is the security skills gap. When finding people with even general security skills is becoming increasingly difficult, finding individuals with specialized skills, such as security analysts, is becoming nearly impossible. But without enough skilled people on the IT staff to analyze the growing volume of data being generated, threats get missed, or they get discovered too late to do anything about them.
Traditionally, ML and AI are used by organizations to perform mundane tasks that bog down security teams, such as correlating log files or performing device patching and updating. But that only scratches the surface of their potential. But Machine Learning (ML) and Artificial Intelligence (AI) can also help fill the cybersecurity skills gap by reducing the complexity and overhead that comes from an expanding security infrastructure. They are perfectly suited for data-oriented tasks, such as the correlation and analysis of log files and threat alerts being generated by an organization’s growing number of security and networking devices.
The Critical Role of Machine Learning
ML-enhanced systems are quite capable of performing higher-order tasks, such as ass ..
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