Digital anomaly detection in data security refers to the process of identifying unusual patterns or behaviors within data that may signify potential threats or security breaches. This method utilizes machine learning and statistical analysis to analyze vast amounts of data in real time, enabling timely detection of fraud, unauthorized access, or system malfunctions. By identifying anomalies quickly, organizations can take proactive measures to mitigate risk and enhance their overall security posture, protecting sensitive information from evolving threats.