Behavioral analytics, long associated with threat detection (i.e. UEBA or UBA), is experiencing a renaissance. Once primarily used to identify suspicious activity, it's now being reimagined as a powerful post-detection technology that enhances incident response processes. By leveraging behavioral insights during alert triage and investigation, SOCs can transform their workflows to become more accurate, efficient, and impactful. Fortunately, many new cybersecurity products like AI SOC analysts are able to incorporate these techniques into their investigation capabilities, thus allowing SOCs to utilize them into their response processes.

This post will provide a brief overview of behavior analytics then discuss 5 ways it's being reinvented to shake up SOC investigation and incident response work.

Behavior Analysis is Back, But Why?

Behavioral analytics was a hot topic back in 2015, promising to revolutionize static SIEM and SOC detections with dynamic anomaly detection to uncover the "unknown unknowns." Within a year, user behavior platforms were quickly acquired by SIEM providers, and soon the concept of a behavioral lens in security data spread across many other detection product categories.

So why is it no longer making waves?

Behavioral analytics is a bit like the microwave in the sense that sometimes the first application of a technology isn't its best one. When American engineer Percy Spencer accidentally discovered microwave technology by noticing chocolate melting in his pocket during a radio technology experiment, he likely had no idea it would go on to revolutionize kitchens worldwide. Initially, microwaves weren't intended for cooking, but over time, their practicality for heating food became obvious, reshaping the way we think about their use. Similarly, behavioral analytics was originally designed as a detection tool in cybersecurity, aimed at spotting threats in real time. However, this early use required extensive setup and maintenance and often overwhelmed security teams with false positives. Now, behavioral analytics has found a far more effective role in post-detection analysis. By narrowing the scope of analysis to provide insights about specific security alerts, it delivers high-value information with fewer false alarms, making it an invaluable part of the incident response process rather than a constant source of noise.

5 Ways Behavioral Analytics is Revolutionizing Incident Response

Here are five key ways behavioral analytics is enhancing incident response, helping security teams respond with greater speed and precision.

1. Improving Accuracy in Incident Investigation

One of the greatest challenges in incident response is sifting through false positives to identify real threats. With post-detection behavioral analytics, analysts can answer key contextual questions that bring clarity to incident investigations. Without understanding how a user, entity, or system normally behaves, it's difficult to discern if an alert indicates legitimate activity or a potential threat.

For example, an "impossible travel" alert, which often creates false positives, flags logins from locations that are humanly impossible to reach in a short time (e.g., a New York login followed by one in Singapore five minutes later). Behavioral baselines and activity provide useful data to effectively evaluate these alerts, such as:

• Is travel to this location typical for this user?
• Is the login behavior usual?
• Is the device familiar?
• Are they using a proxy or VPN, and is that normal?

Behavioral analysis becomes powerful in investigation by providing context that allows analysts to filter out false positives by confirming expected behaviors, especially with alerts like identity which would otherwise be difficult to investigate. This way, SOC teams can focus on true positives with greater accuracy and confidence.

2. Eliminating the Need to Contact End Users

Some alerts, particularly those related to user behavior, require SOC analysts to reach out to end users for additional information. These interactions can be slow, frustrating, and sometimes fruitless if users are hesitant to respond or unclear on what's being asked. By using behavioral models that capture typical patterns, AI-powered SOC tools can automatically answer many of these contextual questions. Instead of waiting to ask users, "Are you currently traveling to France?" or "are you using Chrome?" the system already knows, allowing analysts to proceed without end-user disruptions, which streamlines the investigation.

3. Faster Mean Time to Respond (MTTR)

The speed of an incident response is dictated by the slowest task in the process. Traditional workflows often involve repetitive, manual tasks for each alert, such as digging into historical data, verifying normal patterns, or communicating with end-users. With AI tools capable of performing post-detection behavioral analytics, these queries and checks are automated, meaning analysts no longer need to run slow, manual queries to understand behavior patterns. As a result, SOC teams can triage and investigate alerts in less time, significantly reducing Mean Time to Respond (MTTR) from days to mere minutes.

4. Enhanced Insights for Deeper Investigation

Behavioral analytics enables SOCs to capture a wide range of insights that might otherwise go unexplored. For example, understanding application behavior, process execution patterns (like if it's common to run firefox.exe from a given location), or user interactions can provide valuable context during investigations. While these insights are often difficult or time-consuming to gather manually, SOC tools with embedded post-detection behavioral analytics can automatically analyze and incorporate this information into investigations. This empowers analysts with insights they wouldn't otherwise have, enabling more informed decision-making during alert triage and incident response.

5. Improved Resource Utilization

Building and maintaining behavioral models is a resource-intensive process, often requiring significant data storage, processing power, and analyst time. Many SOCs simply don't have the expertise, resources, or capacity to leverage behavioral insights for post-detection tasks. However, AI SOC solutions equipped with automated behavioral analytics allow organizations to access these benefits without adding to infrastructure costs or human workload. This capability eliminates the need for additional storage and complex queries, delivering behavioral insights for every alert within minutes and freeing up analysts to focus on higher-value tasks.

Figure 1- An example Splunk query that baselines countries that are used by users with the sales department and finds anomalies.

Behavioral analytics and analytics is redefining the way SOCs approach incident response. By shifting from a front-line detection tool to a post-detection powerhouse, behavioral analytics provides the context needed to distinguish real threats from noise, avoid end-user disruptions, and accelerate response times. SOC teams benefit from faster, more accurate investigations, enhanced insights, and optimized resource allocation, all while gaining a proactive edge in threat detection. As SOCs continue to adopt AI-driven behavioral analytics, incident response will only become more effective, resilient, and impactful in the face of today's dynamic threat landscape.

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