AI and Machine Learning in Threat Detection

Machine learning models can process vast volumes of security data and identify complex patterns that would be impossible for human analysts to detect manually. ML excels at anomaly detection, clustering similar events, and predicting which alerts are most likely to represent real threats. Models are trained on historical data to recognize normal patterns, then flag significant deviations. However, ML in security has important limitations: models can be deceived by adversarial inputs (attackers deliberately crafting evasion techniques), they require careful tuning to avoid excessive false positives, and they lack transparency—analysts cannot always understand why a model flagged something. ML is best…

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