Unlike legacy signature-based detection systems, today’s generation of AI-powered security technologies are rarely suited to a plug-it-in-and-watch-it-light-up evaluation strategy. They often include a mix of supervised and unsupervised machine learning, automated threat hunting, trained classifiers, and focus on reducing the number of erroneous and unactionable alerts. As a result, evaluating their detection efficacy requires carefully planned testing before making a buying decision. For products focused on detecting network-borne threats, it is always wise to account...
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