The process of collecting and analyzing data on the behaviors of non-human entities to improve identity management.
Description
Behavioral analytics in the context of Non-Human Identity Management refers to the techniques and processes used to understand and analyze the actions and interactions of non-human entities, such as bots, IoT devices, and automated systems. This approach leverages data collected from these entities to identify patterns, predict future actions, and enhance security protocols. By examining behavior patterns, organizations can distinguish between legitimate and potentially malicious activities, thereby improving their identity management systems. For instance, behavioral analytics can help in detecting anomalies in device usage, such as a sudden spike in API calls from a specific IoT device, which could indicate a security breach. Additionally, it enables more robust authentication mechanisms by analyzing user behavior over time to create a baseline, allowing for the detection of deviations that may signal unauthorized access attempts.
Examples
- Monitoring IoT devices for unusual patterns that may indicate a security threat.
- Using behavioral patterns of automated bots to differentiate between legitimate requests and potential DDoS attacks.
Additional Information
- Behavioral analytics can enhance machine learning models by providing rich datasets.
- It is essential for organizations to comply with privacy regulations when collecting and analyzing behavioral data.