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Explores how AI augments Linux auditing by automating log analysis, configuration checks, and anomaly detection. It outlines integrating ML with auditd, SIEMs, and shell scripts to surface misconfigurations, privilege abuse, and malware indicators. The article covers model selection, data pipelines, alert tuning, explainability, and compliance mapping, with examples and best practices for secure, scalable operations.