AI in Cybersecurity: Practical Defense and Responsible Use

Machine learning models can detect anomalies and adaptive threats at scale, but they must be trained, validated, and observed properly. This article details feature hygiene, model explainability, adversarial testing, and operational monitoring. We include patterns for model retraining, thresholding strategies, and human-in-the-loop workflows to reduce risk. Operational tip: Keep a separate scoring pipeline and make high-impact actions require multi-signal confirmation (model + rules + human review).

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