Insider Security Threats: Why Businesses Need Employee Behavior Analytics

Dmitry Bychkov Analyst at Cinimex When discussing data security in a company’s IT infrastructure, the focus is most often on external threats — an attacker who breaches the corporate network and gains access to internal services containing commercially sensitive information. A much less frequently considered scenario is data compromise without any external intrusion, caused by the actions of the company’s own employees.

In practice, companies use various classes of solutions designed to monitor employee activity and identify such risks. However, these solutions often lack a critical element: the ability to quickly assess whether an employee’s actions are legitimate in a specific context. This remains one of the most challenging aspects of traditional monitoring approaches.

In response to this need, user behavior analytics systems based on machine learning are becoming increasingly common, including the Cinimex SAPP solution. This approach makes it possible to move from formal, rule-based monitoring to the analysis of behavioral patterns. In particular, agentless ML models can be used to reduce the load on infrastructure and work directly with logs from key corporate systems.

The fundamental difference between an ML-based approach and rule-based solutions is the move away from rigid predefined rules. In practice, rules quickly become outdated, fail to account for context, and either remain silent or generate large numbers of false positives. Machine learning models can detect anomalies more accurately and adapt to changes in user behavior over time.

Such systems can identify unusual scenarios, including access to internal resources outside working hours, logins from locations unfamiliar to the employee, unusually active browsing of internal portal sections, and large-scale data exports. Based on accumulated data, the system creates an individual profile of a user’s “normal” behavior, which is used to detect anomalies and can be adjusted as work processes change.

A corporate profile for a department can also be applied to reflect the typical activity of a group of employees. For example, SQL queries against a database performed by a data analyst would be expected, while similar activity by an accountant might warrant additional attention.

False positives are another major challenge. If a model is incorrectly trained or configured, the information security team can be overwhelmed by alerts that provide little practical value and make it harder to respond to genuine incidents. Typical examples include employees who spend most of their time working at a customer’s site and rarely access internal corporate resources, or distributed teams using remote access from different regions and countries. In such cases, failure to properly account for context can distort the actual risk picture.

A properly trained monitoring system makes it possible to move from an abstract alert that “something happened” to meaningful notifications that explain why the alert was triggered. This simplifies incident analysis, reduces the workload on administrators, and speeds up decision-making. Additional notification mechanisms can also help maintain continuous oversight without requiring constant manual monitoring of the system interface.

This is how the approach to monitoring user activity is increasingly being built around information security requirements. Extending behavioral analytics to adjacent areas, including HR monitoring and employee loyalty analysis, is considered a promising direction, but it requires separate consideration of the relevant ethical and regulatory aspects.