In cybersecurity, behavioral analytics is a data-driven method that uses artificial intelligence (AI) and machine learning (ML) to examine trends in user and entity behavior across networks, apps, and other digital environments. Behavioral analytics assists in spotting any security risks that could otherwise go overlooked by spotting patterns and irregularities.
Large volumes of data about how people and entities—like staff members, clients, or Internet of Things devices—interact with a company’s systems are gathered in order to do behavioral analytics.
After that, this data is examined to find typical behavioral patterns and highlight any discrepancies that might point to malevolent or suspicious activity.
How Behavioral Analytics Works
Current user behavior is compared against predetermined behavioral baselines using behavioral analytics. Behavioral analytics can notify security teams of potential vulnerabilities before they become more serious by identifying anomalies, such as a person accessing strange files, logging in at strange times, or using a different device.
For instance, it would be considered a possible breach if an employee who usually works in an office logs in late at night from an unknown place. Beyond conventional rule-based systems, behavioral analytics adds an extra degree of security by analyzing data in real time using AI-driven algorithms.
Key Benefits of Behavioral Analytics
Behavioral analytics can help your organization achieve several important cybersecurity outcomes:
- Real-time threat detection: Automatically alerts IT administrators to suspicious activities as they happen.
- Automated incident response: Automatically disconnects users or entities from the network if a threat is detected.
- Enhanced access security: Strengthens access control by identifying abnormal login attempts and activity patterns.
- Risk reduction: Proactively identifies and mitigates potential insider threats and data breaches.
- Improved user experience: Optimizes the user experience by flagging and resolving security issues without disrupting normal workflows.
Types of Behavioral Analytics
Behavioral analytics can be divided into two primary categories:
- User Behavior Analytics (UBA): Focuses on individual user activity and analyzes patterns to detect anomalies.
- User and Entity Behavior Analytics (UEBA): Extends UBA by monitoring not only users but also entities such as network devices, servers, and IoT devices. UEBA is more comprehensive and capable of identifying complex, multi-layered security threats.
The Difference Between UEBA and UBA
Before the development of UEBA, User Behavior Analytics (UBA) was the go-to cybersecurity tool for monitoring and analyzing user behavior within networks and systems. UBA uses advanced analytics to identify patterns of normal user activity and to detect deviations that could indicate potential security risks.
However, with the rise of Internet of Things (IoT) devices and the growing complexity of modern IT environments, the need for more expansive monitoring capabilities became evident.
Gartner introduced User and Entity Behavior Analytics (UEBA) to fill this gap. Unlike UBA, UEBA tracks and analyzes the behavior of a wide range of entities beyond users, such as routers, endpoints, and servers. UEBA’s ability to monitor IoT devices individually or in peer groups makes it more effective at detecting threats in multi-device ecosystems.
UEBA also enhances security monitoring for cloud environments, where traditional tools struggle to keep up. By analyzing behavior across cloud-based assets, UEBA helps organizations detect suspicious activity that might indicate a breach or a misconfiguration in remote environments.
What to Look for in Behavioral Analytics Tools
When evaluating behavioral analytics tools, it’s essential to ensure they provide the following features:
- User behavior tracking: Capture and analyze what users click, where they encounter friction, and how they respond to changes in their environment.
- Funnel analysis: Understand how users move through steps to complete actions, such as making a purchase or signing into a secure system.
- Heatmaps: Visualize user interaction with your website or application to identify pain points, bugs, and areas of high activity.
- Customer behavior insights: Leverage data to personalize customer service and improve satisfaction by addressing users’ specific needs.
Behavioral Analytics vs. SIEM
Although both behavioral analytics and Security Information and Event Management (SIEM) solutions are essential to cybersecurity, they focus on different aspects of data:
- Behavioral Analytics: Uses machine learning to analyze user interactions, detecting deviations from normal behavior to provide proactive alerts. It’s most effective for detecting insider threats and compromised accounts.
- SIEM: Primarily collects and correlates security event data, providing a broader view of system logs and events. SIEM uses rule-based correlation and pattern recognition to identify potential threats.
However, the two technologies can complement each other. Many SIEM solutions now include UEBA modules, allowing organizations to enhance their security posture by integrating behavioral analytics within the SIEM framework.
Best Practices for Implementing
To ensure the successful implementation of a behavioral analytics solution in your organization:
- Train your staff: Ensure that IT and security personnel are well-versed in how behavioral analytics works and how to act on its insights.
- Consider insider threats: Use behavioral analytics to detect insider threats that traditional security tools might miss.
- Use complementary tools: Behavioral analytics works best when integrated with other security tools like SIEM, endpoint detection and response (EDR), and network detection and response (NDR).
The Future
The development of AI and ML technologies will have a significant impact on behavioral analytics in the future.
It will get even more important as these technologies advance, enabling businesses to identify ever-more-sophisticated dangers and make data-driven decisions.
Businesses may be able to better tailor consumer experiences and fortify cybersecurity safeguards by using predictive analytics to foresee user requirements and security threats before they materialize.
Furthermore, the development of zero trust security models—where ongoing verification is necessary to preserve network security—will be greatly aided by behavioral analytics.
The insights required to maintain the zero trust philosophy can be obtained using these new analytics, which tracks user activity and instantly identifies irregularities.
All the views in this article are of SECURONIX and The Volt Post takes no editorial responsibility for the opinions mentioned above.
