If these tools are providing more accurate and timely results than humans can provide, why isn’t everyone relying on them? “The fact of the matter is that they still aren’t perfect,” said McReynolds. There are a lot of requirements for these tools to run smoothly and effectively.
Data, Data, Data: Machine learning is data intensive because decisions are made based on data, not just algorithms. These tools need approximately 10 times the amount of data in order to understand the details and dimensions of network behavior. Learning this amount of data takes time.
$$: Machine learning-based applications carry a cost premium, as does the talent necessary to operate and optimize them. These resources are in short supply and high demand, which can make them quite expensive.
One Tool in the Toolkit: Machine learning is one tool amongst many. We need to remember that there are traditional or core cybersecurity concepts that are integral to protecting our network, McReynolds said. “There is no silver bullet when it comes to security.”
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