Insider Threat Detection with Behavior Analytics

Introduction

Insider threats pose a significant risk to organizations of all sizes. As an employee or contractor with privileged access, a malicious insider is inherently difficult to detect using traditional security tools. Behavior analytics provides a powerful approach to detecting risky user behaviors indicative of insider threats. In this article, I will provide an in-depth look at using behavior analytics for insider threat detection.

What is Insider Threat Detection?

Insider threat detection involves identifying malicious activities by insiders who may intentionally or unintentionally cause harm to an organization. An insider threat can include:

• Malicious insiders – Employees or contractors who intentionally steal data, sabotage systems, or cause other damage.

• Negligent insiders – Insiders who unintentionally expose sensitive information or cause a data breach due to poor security practices.

• Compromised insiders – Insiders who have their credentials or devices compromised by external threats to gain unauthorized access.

The challenge is that insiders naturally have trusted access to an organization’s most critical assets, making their malicious actions difficult to detect using traditional perimeter security tools.

The Role of Behavior Analytics

Behavior analytics analyzes patterns in user activities to identify anomalies that may represent insider threats. By developing behavioral profiles for each user, abnormal behaviors like unauthorized access attempts, suspicious data transfers, and policy violations can be detected.

Some key capabilities of behavior analytics for insider threat detection include:

• User profiling – Developing historical profiles of each user’s typical activities.

• Activity monitoring – Logging detailed user activities across all systems and devices.

• Anomaly detection – Detecting significant deviations from normal profiles that may indicate potential threats.

• Risk scoring – Calculating risk scores for users based on anomalous behaviors.

• Alert triage – Intelligently prioritizing anomaly alerts to focus on the highest risk users.

Constructing Behavioral Profiles

The first step in insider threat detection with behavior analytics is to construct behavioral profiles for each user. The profiles characterize a user’s standard patterns of activity over time.

Some elements that provide insight into normal behaviors include:

• Logins – Locations, times, devices used for logins

• Data access – Normal data resources accessed, times, volume

• Email habits – Frequency, recipients, attachments

• Network use – Normal servers, IP addresses connected to

• Policy compliance – Acceptable use policy adherence

The profiles provide a baseline for comparing each user’s daily activities. Even small changes, like logging in from an unusual location, can trigger an alert for investigation.

Detecting Anomalous Behaviors

With the profiles in place, the behavior analytics solution can detect anomalies that deviate significantly from a user’s normal activities. Suspicious anomalies might include:

• Unauthorized access attempts – Accessing restricted data or systems

• Credential sharing – Multiple logins with one user account

• Suspicious data transfers – Large or unusual data uploads and downloads

• Policy violations – Visiting prohibited websites, interference with monitoring tools

• Workplace disturbances – Threats or signs of disgruntlement

Each anomalous behavior is assigned a risk score based on the severity of deviation from normal patterns.

Prioritizing Insider Threat Alerts

The presence of an anomalous behavior does not necessarily indicate malicious intent. Users may inadvertently trigger alerts through normal work activities.

Behavior analytics uses intelligent alert prioritization to minimize false positives and focus on the highest risk alerts. Prioritization methods include:

• Aggregating risk scores – Combining multiple anomalies to identify unusually high risk

• User risk profiling – Considering a user’s role, behavior history, and other factors

• Triaging alert context – Reviewing circumstances around the anomalous activity

• Collaboration – Leveraging human review to validate the most suspicious threats

This allows actual malicious insiders to be distinguished from innocent anomalies.

Conclusion

Detecting insider threats is a critical security challenge facing modern organizations. Behavior analytics provides a powerful approach to identify suspicious user activities indicative of insider threats. By developing profiles of normal behavior and detecting meaningful anomalies, malicious insiders can be spotted before they cause damage. With intelligent prioritization of alerts, behavior analytics can become an integral part of an organization’s insider threat prevention program.