How to Leverage User Behavioral Analytics in Cybersecurity


Traditional security approaches, such as antivirus software and firewalls, while crucial, no longer suffice in the face of increasingly sophisticated cyber attacks. MSPs need a proactive approach that not only secures their clients’ network perimeter and responds to internal threats, but also monitors user accounts and detects anomalous behavior.

A survey by the SANS Institute found that 35% of respondents lack visibility into insider threats. Analyzing user behavior is essential to understanding how users interact with systems, applications and data. By harnessing the power of data analysis and machine learning, user behavior analysis (UBA) empowers MSPs to detect anomalies, mitigate risks and optimize security posture.

Let’s explore the relevance of user behavioral analysis in cybersecurity — how it works and why it is essential for a comprehensive security strategy.

What Is User Behavior Analytics?

In cybersecurity, user behavior analytics focuses on monitoring and analyzing the activities of users within an organization’s network or applications. UBA analyzes user data from various sources, such as:

  • System logs
  • Network logs
  • Application logs

The primary goal of behavioral analysis is to identify and mitigate security breaches by detecting deviations from established behavior patterns. UBA also provides a holistic view of user activity across multiple systems and tools to achieve this goal of enhanced security.

Example of Behavioral Analysis in Cybersecurity

Let’s say you leverage SaaS Alerts to secure your clients’ systems. In one of the client’s application logs, you notice an anomaly. An employee, John, typically accesses financial transaction records during business hours and only from approved locations.

The UBA system, however, detects that John is accessing sensitive information late at night from an unfamiliar location. This deviation triggers a security alert and provides details about the login.

deviation in user behavior analytics on SaaS Alerts dashboard

You promptly notify the client about the situation and take action to mitigate the threat, such as temporarily blocking John’s access, changing his credentials and launching a comprehensive security review to ensure no data breaches have occurred.

In this case, UBA detects suspicious user behavior, allowing you to respond quickly to a potential security threat and safeguard customer information.

Difference Between UBA and UEBA

User behavior analytics (UBA) and user and entity behavior analytics (UEBA) are related concepts in cybersecurity, but they have distinct differences. While UBA focuses on individual user behavior, UEBA also factors in the behavior of entities like devices, servers and applications within a network. 

Here’s the breakdown:

Aspect User Behavior Analytics User and Entity Behavior Analytics
Scope Primarily focuses on individual user behavior and activities. Analyzes users and entities (devices, applications, servers) and their interactions with each other.
Threat Detection Effective in identifying insider threats, compromised accounts and unauthorized user access. Useful in detecting complex threats involving multiple entities such as malware-infected devices and unusual server interactions.
Data Sources Relies on data located in the organization’s SIEM or SOAR system. Looks at data from endpoint detection and response systems as well as threat intelligence feeds.
Use Cases Valuable for securing user accounts and detecting user-related threats. Suitable for identifying a wide range of threats, including those involving multiple entities and complex attack vectors.
Complexity of Deployment Simpler to deploy due to its user-centric nature. Requires extensive deployment and configuration.

Importance of User Behavioral Analysis in Cybersecurity

Incorporating UBA into the cyber strategy strengthens the overall security posture and helps to prevent data breaches, financial losses and reputational damage.

Here’s why behavioral analytics is important:

1. Proactive Threat Detection

Insider threats, whether unintentional or intentional, are a significant concern. The Verizon 2023 Data Breach Investigation Report found that 74% of data breaches involve a human element, such as privilege misuse, stolen credentials or social engineering.

UBA can detect unusual activities by trusted insiders, such as employees or contractors, who may abuse their access privileges or have their accounts compromised. By continuously monitoring user behavior, it identifies deviations from established patterns, helping to spot indicators of compromise and signs of compromise early.

2. Adaptive Security

UBA relies on machine learning models to improve its adaptability to evolving automated threats. It learns from historical data and continuously adjusts its understanding of what constitutes “normal” behavior. This adaptability is essential to deal with sophisticated attack techniques.

3. Reduction in False Positives

UBA reduces the number of false positives by focusing on behavior rather than just signature-based detection. It considers factors such as user roles, location, time and application access to enhance accuracy. This contextual analysis enables security teams to concentrate on genuine threats and reduce alert fatigue.

4. Compliance and Reporting

Non-compliance with industry regulations leads to direct losses from business disruption and impacts future revenue. According to Drata, four out of five organizations deal with the following negative consequences due to non-compliance:

  • Slower sales cycles (41%)
  • Security incidents (40%)
  • Fines (24%)

UBA aids in meeting regulatory compliance requirements by providing detailed logs and reports of user activities. This reporting is crucial for industries with strict data protection and privacy regulations.

5. Incident Response

The continuous monitoring and alerting capabilities empower security teams to investigate threats and implement mitigations with minimal delay.

User behavioral analysis accelerates incident response because it tracks which data was accessed by whom and when. It also shows how the information was used, modified or deleted. This information is essential to understand the nature and extent of an attack and implement long-term remediation efforts by pinpointing suspicious activity patterns.

Tips for Implementing Behavioral Analytics in Cybersecurity

Implementing behavioral analytics in cybersecurity requires careful planning and execution to maximize effectiveness. Here are the top five tips for a successful implementation:

  1. Determine objectives and use cases: Identify the specific threats or challenges to address. Whether it’s insider threats, business email compromise or advanced persistent threat (APT) detection, having a well-defined purpose ensures your UBA systems meet security goals effectively.
  2. Collect and integrate data: Gather data from various sources across your network, including logs from applications, network traffic and user access. Ensure that the data collected is comprehensive, accurate and up to date.
  3. Create and refine security baselines: Establish baselines of normal behavior for users. Initially, this step may involve historical data analysis, but over time, refine these baselines using machine learning and AI algorithms. Baselines should be role-specific and consider factors such as working hours, access patterns and locations.
  4. Tune the threshold setting: Fine-tune your behavioral analytics system by setting appropriate thresholds for anomaly detection. It’s essential to balance between not missing real threats and minimizing false positives.
  5. Integrate with existing security systems: Incorporating UBA into pre-existing systems such as antivirus, firewalls and intrusion detection systems enables data sharing and correlation. UBA can consume data generated by these tools, adding another layer of analysis.

SaaS Alerts: Trusted Partner for User Behaviour Analysis

SaaS Alerts empowers MSPs like you with advanced behavioral analytics in cybersecurity. With our SaaS security software, you gain deeper insights into your clients’ user activities and significantly improve threat detection.

We help you supercharge your clients’ security strategy with the following capabilities:

  • Comprehensive user monitoring: SaaS Alerts provides a comprehensive view of user behavior, allowing you to monitor activities and detect anomalies.
  • Customized alerting: Our platform allows you to tailor alerts to your clients’ specific use cases and security requirements. This capability ensures you only get alerts about important events.
  • Integration with existing tools: We offer seamless integration with your existing MSP tools, enabling a more cohesive approach to cybersecurity.
  • Advanced machine learning: SaaS Alerts leverages machine learning to adapt to evolving user behaviors, boosting your threat detection capabilities.

Start your free trial to see how SaaS Alerts bolsters cybersecurity through user behavior analysis.

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