5 Ways Behavior Analytics Is Revolutionizing Incident Response

Behavioral analytics, long associated with threat detection (e.g. UEBA or UBA), is experiencing a renaissance. Previously used primarily to identify suspicious activity, it is now being reimagined as a powerful post-detection technology that improves incident response processes. By leveraging behavioral insights to triage and investigate alerts, SOCs can transform their workflows to be more accurate, efficient and effective. Fortunately, many new cybersecurity products such as AI SOC analysts are able to integrate these techniques into their investigative capabilities, allowing SOCs to leverage them in their response processes.

This post provides a brief overview of behavioral analysis and then discusses five ways it is being reinvented to revolutionize SOC investigations and incident response.

Behavioral analysis is back, but why?

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

So why is it no longer making waves?

Behavioral analysis is a bit like microwaves in the sense that sometimes the first application of a technology isn’t the best. When American engineer Percy Spencer discovered microwave technology by chance when he noticed chocolate melting in his pocket during a radio experiment, he probably had no idea that it would later revolutionize kitchens around the world. Originally, microwaves were not intended for cooking, but over time their practicality for heating food became apparent and changed the way we think about their use. Likewise, behavioral analysis was originally developed as a cybersecurity detection tool aimed at detecting threats in real-time. However, this early deployment required extensive setup and maintenance and often overwhelmed security teams with false alarms. Behavioral analysis has now found a much more effective role in post-detection analysis. By narrowing the scope of analysis to provide insights into specific security alerts, it delivers high-quality information with fewer false positives, making it an invaluable part of the incident response process rather than a constant source of noise.

5 Ways Behavior Analytics Is Revolutionizing Incident Response

Here are five key ways behavioral analytics improve incident response and help security teams respond faster and more accurately.

1. Improve incident investigation accuracy

One of the biggest challenges in incident response is sorting 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 typically behaves, it is difficult to determine whether an alert indicates legitimate activity or a potential threat.

For example, a “travel impossible” warning, which often results in false positives, flags registrations from locations that are impossible for people to reach in a short period of time (e.g. a registration in New York followed by a registration in Singapore in five minutes later). Behavioral baselines and activities provide useful data to effectively evaluate these alerts, such as:

• Is the journey to this location typical for this user?
• Is the login behavior common?
• Is the device known?
• Are they using a proxy or VPN, and is this normal?

Behavioral analysis becomes highly useful in investigations because it provides context that allows analysts to filter out false positives by confirming expected behaviors, particularly for alerts such as identity that would otherwise be difficult to investigate. This allows SOC teams to focus on truly positive outcomes with greater accuracy and confidence.

2. Elimination of the need to contact end users

Some alerts, particularly those related to user behavior, require SOC analysts to ask end users for additional information. These interactions can be slow, frustrating, and sometimes unsuccessful when users are hesitant to respond or are unclear about what is 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 for users to ask, “Are you currently traveling to France?” or “Are you using Chrome?” the system already knows this and allows analysts to proceed without interruptions to the end user, streamlining the investigation.

3. Faster Mean Response Time (MTTR)

The speed of an incident response is determined by the slowest task in the process. Traditional workflows often involve repetitive, manual tasks for each alert, such as: For example, searching historical data, checking normal patterns, or communicating with end users. With AI tools capable of post-detection behavioral analysis, these queries and checks are automated, eliminating the need for analysts to run slow, manual queries to understand behavioral patterns. This allows SOC teams to triage and investigate alarms in less time, significantly reducing mean time to response (MTTR) from days to just minutes.

4. Advanced insights for deeper investigations

Behavioral analytics enable SOCs to capture a wide range of insights that might otherwise remain unexplored. For example, understanding application behavior, process execution patterns (e.g., if it is common to run firefox.exe from a specific location), or user interactions can provide valuable context in investigations. While it is often difficult or time-consuming to collect these insights manually, SOC tools with embedded behavioral analytics can automatically analyze and integrate this information into investigations once detected. This gives analysts insights they wouldn’t otherwise have, enabling more informed decision-making in alarm triage and incident response.

5. Improved resource utilization

Building and maintaining behavioral models is a resource-intensive process that often requires a lot of data storage, computing power, and analyst time. Many SOCs simply do not have the expertise, resources, or capacity to leverage behavioral insights for post-detection tasks. However, AI SOC solutions equipped with automated behavioral analysis enable companies to realize these benefits without increasing infrastructure costs or human workload. This feature eliminates the need for additional memory and complex queries, provides behavioral insights for each alert in minutes, and frees analysts to focus on higher-value tasks.

Figure 1: An example Splunk query that discovers countries used by sales department users and finds anomalies.

Behavioral analytics and analytics are redefining the way SOCs approach incident response. By transforming from a front-line detection tool to a post-detection powerhouse, behavioral analytics provides the context needed to distinguish real threats from noise, avoid disruption to end users and accelerate response times. SOC teams benefit from faster, more accurate investigations, improved insights and optimized resource allocation, 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 given today’s dynamic threat landscape.

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