Threat Detection Feature in miniOrange PAM

The Threat Detection feature in miniOrange Privileged Access Management (PAM) is designed to enhance security by identifying, analyzing, and blocking potential threats in real-time. The feature leverages advanced algorithms, including User Behavior Analytics (UBA), to continuously monitor privileged activities, detect anomalies, and take immediate actions such as blocking suspicious activities, sending alerts to administrators, and generating detailed reports.

This document provides a comprehensive overview of the Threat Detection feature in miniOrange PAM, including its core functionality, architecture, algorithms, and the configuration steps for administrators. Additionally, the document includes diagrams to help better understand the technical flow and structure of the feature.

Step-by-step guide for Threat Detection in miniOrange PAM"

1. Features and Capabilities

1.1 User Behavior Analytics (UBA)

User Behavior Analytics (UBA) is the cornerstone of the Threat Detection feature. It monitors the activity of privileged users and compares it against established baselines of normal behavior. Any deviation from this baseline triggers the detection of potential threats. UBA relies on machine learning algorithms to build dynamic models of user behavior, allowing the system to adapt to changing user patterns over time.

1.2 Anomaly Detection

Anomalies are detected by analyzing a wide variety of parameters, such as:

Login times: Unusual login times compared to historical data.
Access locations: Accessing the system from new or geographically distant locations.
Unusual access patterns: Accessing resources that the user has never interacted with before.
Privileged commands: Executing commands that are highly privileged or have been rarely used.
Resource usage patterns: Abnormal consumption of system resources by a user.

When such anomalies are detected, miniOrange PAM uses real-time monitoring to trigger alerts, blocking actions, or even session termination if necessary.

1.3 Advanced Threat Detection Algorithms

miniOrange PAM employs a variety of algorithms for detecting threats:

Clustering algorithms: Group user activities into clusters based on similar behavior. Outliers or activities that deviate significantly from these clusters are flagged as potential threats.
Statistical analysis: Analyzing user activity statistics (e.g., login frequency, duration, and success rate) to detect unusual patterns.
Correlation algorithms: Correlates events from different sources (such as logs, user actions, and system activities) to identify hidden patterns that indicate malicious behavior.
Machine Learning: Machine learning models continuously refine the detection process. These models learn from historical data, adapt to new user behaviors, and reduce false positives over time.

1.4 Real-Time Threat Mitigation

Once a potential threat is detected, the system performs immediate actions:

Alerts to Admins: Admins are notified via email, SMS, or in-app notifications about detected threats. These alerts contain detailed information about the anomaly, including user details, affected resources, and severity levels.
Blocking Suspicious Activities: Threats can be blocked automatically by terminating the user session, locking the user account, or restricting access to certain resources.
Reporting and Audit Logs: Detailed reports and logs are generated for each detected threat, providing insights into the nature of the attack and the actions taken by the system.

1.5 Threshold-based Detection

miniOrange PAM allows administrators to configure threshold limits for specific user activities. For example, if a user attempts to log in more than five times within an hour with incorrect credentials, the system may flag this as a brute force attack.

2. Architecture of Threat Detection Feature

The architecture of the Threat Detection feature in miniOrange PAM involves multiple components that work together to continuously monitor and respond to potential threats.

2.1 Core Components

2.1.1 User Activity Monitoring Module

The User Activity Monitoring module captures all actions performed by privileged users, including logins, command executions, and resource accesses. It then sends this data to the Threat Detection Engine for further analysis.

2.1.2 Activity Log Collection Module

This module stores activity logs for each user and their interactions with the system. The logs are analyzed by the Threat Detection Engine to detect any unusual activities.

2.1.3 Threat Detection Engine (TDE)

The TDE is the central component that combines User Behavior Analytics (UBA), machine learning algorithms, and correlation techniques to detect potential threats. It compares the ongoing user activities against predefined baselines and generates threat alerts when suspicious activities are identified.

2.1.4 Threat Response Module

The Threat Response Module executes predefined actions when a threat is detected. These actions include:

Terminating or blocking user sessions.
Sending alerts to administrators.
Generating detailed reports.

2.1.5 Admin Notification System

Once a threat is detected, the Admin Notification System sends alerts to the administrator's preferred channels (email, SMS, or in-app notifications) with relevant details about the threat, enabling the administrator to take further action if necessary.

3. Workflow of Threat Detection

3.1 Step-by-Step Workflow

User Activity Monitoring: The system continuously monitors the privileged users' activities.
Activity Log Collection: Each activity is logged and stored for future analysis.
Threat Detection Engine (TDE): The logs and real-time data are passed to the TDE, which uses UBA and advanced algorithms to detect anomalies and suspicious behavior.
Threat Detection: The TDE compares real-time user activity against historical data to identify deviations or threats.
Alerting and Blocking: If a threat is detected, an alert is immediately sent to the admin. Depending on the severity, the system may block the user's session or restrict access to certain resources.
Log Generation: A detailed report of the detected threat, actions taken, and system logs is generated and stored for auditing purposes.

4. Configuration and Customization

Administrators can configure the Threat Detection feature through the miniOrange PAM Admin Console. The following configurations are available:

4.1 Configuring User Behavior Baselines

Administrators can set up behavior baselines for each privileged user or group. These baselines will define what is considered "normal" activity, allowing the system to detect deviations effectively.

4.2 Setting Threshold Limits for Alerts

Threshold limits for various activities (e.g., failed login attempts, resource access attempts) can be customized to fit organizational policies. Once these thresholds are exceeded, the system will automatically trigger alerts and responses.

4.3 Defining Threat Response Actions

Administrators can configure actions such as:

Blocking access to certain applications or servers.
Locking user accounts.
Terminating sessions.
Sending alerts to specific administrators or security teams.
Changing Password of accounts which are under threat.

Conclusion

The Threat Detection feature in miniOrange PAM provides a comprehensive solution for identifying and mitigating risks associated with privileged user activities. By leveraging advanced algorithms and User Behavior Analytics, the system can automatically detect and respond to potential threats in real-time, ensuring the highest level of security for privileged accounts.

This document has outlined the core components, architecture, and workflow of the Threat Detection feature, providing administrators with a clear understanding of how it works and how to configure it for their organization's needs.

For further support, contact pamsupport@xecurify.com or refer to the miniOrange PAM user guide for detailed instructions on configuring and using the Threat Detection feature.

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