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New technologies in machine learning (ML) and artificial intelligence (AI) offer the potential for improved detection and identification. However, existing ML models focus on data set labeling on non-human readable features, which results in the detection of anomalous behavior with general classifications that cannot be interpreted by a SOC analyst. Enter “Explainable Machine Learning.”