Statistical technique for analyzing data points collected over time to identify trends, patterns, cycles, and anomalies in temporal behavior sequences.
Time-series analysis is crucial for insider threat detection as it reveals temporal patterns in user behavior such as unusual working hours, periodic data access spikes, or gradual changes in activity levels that may indicate threat progression. The technique can identify seasonal patterns (legitimate business cycles) versus anomalous temporal behaviors (potential data theft campaigns). Advanced applications include detecting slow-moving insider threats that evolve over weeks or months and identifying trigger events that correlate with behavioral changes.