ActivTrak collects behavior data to feed AI-driven analytics that profile workers and predict behavior patterns. The platform can operate in silent mode without worker awareness. Extensive data retention creates permanent behavioral records used for workforce assessments.
| Data Type | Collected | Shared | Retention |
|---|---|---|---|
| Activity Patterns | Continuous monitoring | Employer + AI | Indefinite |
| Application Usage | Every app opened | Employer analytics | Full history |
| Website Visits | All URLs | Employer + reports | Indefinite |
| Behavior Models | AI-generated profiles | Employer insights | Persistent models |
| Risk Assessments | Anomaly detection | Security alerts | Indefinite |
Can collect data without any visible indicator to workers. Employees may be comprehensively monitored without knowing monitoring is active.
Machine learning creates behavioral models that persist over time. These profiles predict future behavior and identify "anomalies" based on opaque algorithms.
Years of activity data accumulate into comprehensive behavioral archives that could influence future employment decisions long after collection.
Risk detection algorithms may flag innocent behavior as suspicious, creating permanent records of false positives that damage worker reputations.
Individual data is compared against team averages and benchmarks, creating competitive surveillance that pits workers against each other.
Offers options to exclude certain applications or sites from monitoring, though configuration is controlled by employers, not workers.