Behavioral analytics has become increasingly important in multiple aspects of managing and protecting an organization’s digital resources. The effective use of behavioral analytics enables a company to better understand how employees act in its IT environment and how customers interact with its websites or products.
The knowledge gained from behavioral analytics can be instrumental in enhancing IT security and promoting company growth.
This article discusses the different types of behavioral analytics, the benefits organizations can expect to achieve through its use, and how it promotes IT security. The effective use of behavioral analytics provides information that is critical to protecting data resources from the risks of external and internal threat actors.
In this article:
What is behavioral analytics?
Behavioral analytics is a discipline of data analytics that concentrates on understanding and predicting human behavior. The practice involves the collection of data from multiple sources to identify patterns and trends in the behavior of a specific group of individuals.
Studying these patterns can provide insights into how members of a group act in a given situation and can help predict their future actions.
In the IT world, behavioral analytics is used for two important but very different purposes. Organizations typically implement behavioral analytics for one of the following reasons.
Types of behavioral analytics
Multiple types of behavioral analytics exist to address various objectives when studying human behavior. They rely on different methods and techniques to collect various types of data in an attempt to understand and decipher behavior. Organizations typically employ a combination of several types of behavioral analytics to achieve specific business results.
The following are some of the common types of analytics used to acquire insight into human behavior and provide the basis for making more effective and informed decisions.
- Descriptive analytics analyzes historical data to understand past actions and outcomes. Data is summarized and visualized to recognize patterns and relationships in behavior. The insights derived from descriptive analytics may be used as the foundation for more advanced types of analytics.
- Predictive analytics makes predictions regarding future behavior by analyzing historical data. Trends and correlations identified in previous behavior can be used to forecast probable future outcomes. Predictive analysis is often used to address business objectives such as proactively meeting customer expectations by staying in step with market trends.
- Prescriptive analytics builds on predictive analytics and recommends specific strategies or tactics to influence, rather than simply predict future outcomes. Advanced technological solutions such as artificial intelligence (AI) and machine learning (ML) use models built on prescriptive analytics to determine the best path forward to meet business objectives.
- Diagnostic analytics strive to understand the underlying reasons behind events or behaviors. It analyzes relationships and contributing factors to identify the root cause of a particular behavior or action. The use of diagnostic analytics is useful for understanding what drives specific types of behavior so modifications can be made to regulate unwanted activities or promote desired outcomes.
- Fraud analytics concentrates on analyzing behavioral patterns and anomalies to detect and prevent fraud. This type of analytics uses statistical analysis and machine learning algorithms to locate suspicious actions that deviate from typical patterns. Organizations employ fraud analytics to guard against financial losses and identify potentially fraudulent activities.
- Real-time analytics analyzes real-time data to offer immediate insights and responses to activities as they occur. The ability to perform effective real-time analytics is essential in protecting against risky behavior initiated by threat actors or unwitting insiders. The insights garnered from real-time analytics allow for swift preventative actions to address any threats and maintain security.
Collecting data for behavioral analytics
The information used as input for behavioral analytics can come from a wide variety of sources. Advanced analytical solutions will build baselines that define normal or acceptable actions or behaviors. These baselines are then used to identify anomalous or suspicious activities that may indicate threats and need to be investigated.
The following common structured and unstructured data sources are typically used to provide the raw data for behavioral analytics. Different sources provide more effective data for studying the behavior of either the users or customers within an IT environment.
IT users
Behavioral analytics of IT users is primarily done to protect IT resources from internal or external threats. The information necessary for performing effective behavioral analytics on both authorized and unauthorized users primarily comes from the following sources.
- Operating system and application logs furnish data on user activity throughout an IT environment to identify suspicious activity. Access to this information is essential when using behavioral analytics in a cybersecurity solution. Advanced real-time analytics processes this information and can offer an immediate response to perceived threats to mitigate risks to the business.
- Operational data such as metrics related to system performance can provide valuable information that can be correlated with user activity and used as input for many types of behavioral analytics. For example, prescriptive analytics can combine performance and behavioral data to determine the best way to protect and optimize IT resources.
- Network activity logs offer data that can be used to identify abnormal activity that may indicate the presence of a threat actor. Attempts to transfer large volumes of data or send files to unknown addresses can be identified by analytics. Real-time analytical tools can take action to quickly negate the threats and minimize risks.
Customers
Behavioral analytics of customers is usually done to increase engagement and offer enhanced services.
The following sources offer valuable information to better understand customer behavior through analytics.
- Website data furnishes information on user activities such as page views, session duration, and navigation paths. This data monitors customer preferences and can be used to develop more engaging and satisfying online experiences.
- Transactional data such as customer orders, subscriptions, and payments provide insight into purchasing patterns and behavior that can be used to tailor future offerings.
- Customer interaction data can be obtained from Customer Relationship Management (CRM) systems as well as less formal email and text messaging channels. This information can be used to gauge preferences and satisfaction to enhance customer service and improve brand identity.
- Additional data sources include social media platforms, surveys, and customer feedback. This unstructured data may be hard to integrate into some analytical solutions.
What are the benefits of behavioral analytics?
Incorporating behavioral analytics provides organizations with multiple benefits that address a wide variety of business objectives.
The following are some of the more impactful and common benefits of employing software tools capable of performing behavioral analytics.
- Improving cybersecurity and data protection - Insights obtained from behavioral analytical solutions enable security teams to identify suspicious activity that may indicate a risk. Real-time analytics enables proactive measures to be taken immediately to address the threat and improve cybersecurity.
- Identifying insider threats - Anomalous behavior detected by analytics can be instrumental in pointing out both malicious and unintentional insider threats. An organization can then take the necessary actions and provide training or discipline as necessary.
- Optimizing operational efficiency - Operational efficiency can be affected by behavioral patterns discovered through analytics. Management can use analytics to streamline operations and ensure the business runs efficiently.
- Enhancing decision-making - Companies can use behavioral analytics to drive decision-making to promote products or services in ways that appeal to their customers. Analytics can also be helpful when making internal decisions regarding issues such as staffing and employee training.
- Enabling regulatory compliance - Analytics can help find compliance gaps by searching for anomalous behavior that impacts the handling of sensitive and regulated data. Proactively addressing the gaps can avoid the legal and financial repercussions of noncompliance.
- Satisfying customer expectations - Companies must address customer expectations to survive in today’s competitive business landscape. Behavioral analytics offers information on purchasing decisions and preferences that enable companies to create more satisfying experiences to attract and retain business.
- Gaining a competitive advantage - Insights into customer behavior and satisfying their expectations can give a company a substantial competitive edge. For example, they can be first to market with products that address emerging trends and customer demands.
Behavioral analytics has become an integral component in enhancing IT security and an organization’s ability to secure sensitive and high-value resources from threat actors.
Two main categories of behavioral analytics solutions have emerged and are available to companies looking to provide enhanced security from internal and external threats.
User behavior analytics (UBA) is focused on using monitoring systems to track, collect, and analyze user behavior in IT systems. Limiting monitoring to human users ignores the many other types of entities that can interact with an IT environment.
User entity and behavior analytics (UEBA) encompasses monitoring IT devices, processes, and applications for more complete visibility into potential threats.
For our discussion, we will use the term UEBA with the understanding that the same functionality, limited to user activity, is also available in UBA tools.
All UEBA systems are built using three main components.
The three pillars of UEBA
- Data collection and aggregation - UEBA platforms gather and aggregate data from multiple sources within the IT environment. Sources include logs from networks, servers, devices, and other infrastructure components. More diverse and comprehensive data sources lead to enhanced effectiveness by providing additional information to analytics engines.
- Behavioral analytics - Machine learning algorithms and statistical models are used to implement advanced analytical techniques on the collected data. The tools analyze data and define baselines of normal behavior for users and entities. Continuous monitoring and analysis of behavior across the environment detect suspicious activities or abnormal patterns that are signs of policy violations or security threats. Machine learning enables the tool to evolve to address emerging threats and modify baselines when necessary.
- Risk scoring and use cases - Alerts generated by the tool’s analytics engines need to be scored and classified so they can be used effectively by security analysts. Scoring should be based on various factors including the severity, potential impact, and probability that the detected behavior is risky. Tools may be configured to look at the signs of specific cybersecurity use cases such as malicious insiders, compromised user credentials, and advanced persistent threats (APTs).
UEBA tools are often used alongside other security solutions such as security information and event management (SIEM) tools.
Examples of UEBA tools in action
The following examples illustrate some of the common types of suspicious and malicious behavior UEBA tools can help to identify.
- Compromised accounts - Accounts demonstrating strange or abnormal behavior may indicate they have been compromised and are being used by threat actors for data exfiltration or other nefarious purposes.
- Insider threats - A UEBA platform can locate potential insider threats by identifying users attempting to gain access to restricted data or systems not needed for business purposes.
- Fraudulent account administration - Unusual account creation or modification discovered by UEBA tools may indicate a malware infection that is creating fraudulent credentials.
- Advanced persistent threats - Advanced persistent threats attempt to remain hidden in an environment until they find an appropriate target to exploit. UEBA software can identify the subtle lateral movements through the environment that often accompany APTS.
How data loss prevention software leverages behavioral analytics
Standalone UEBA applications may not take action when discovering anomalies. Their purpose is to alert administrators to issues that deserve their attention. While this functionality is certainly important and can help mitigate cyber threats, it can be improved by automating responses when unacceptable behavior is detected.
Data loss prevention (DLP) software leverages behavioral analysis as it works to protect an organization’s valuable information. The platform identifies abnormal or suspicious behavior as it relates to a company’s sensitive or high-value data to prevent its deliberate or accidental misuse.
A DLP solution enhances UEBA functionality by automatically taking action when valuable data is at risk.
The Reveal platform by Next is a modern DLP platform that incorporates the capabilities of a UEBA tool in a comprehensive data loss prevention solution. Reveal automates the enforcement of an organization’s data handling policy to ensure the security and integrity of high-value and sensitive information and can restrict the unapproved use of company data by anyone in the organization.
The following features make Reveal an effective choice in a UEBA and DLP tool.
- Reveal’s next-gen endpoint agents are powered by machine learning that identifies and categorizes data at the point of risk. Baselines are created at deployment and multiple behavioral analytics algorithms are used to define typical versus anomalous behavior.
- Reveal is a cloud-native solution built with cutting-edge technology that enables speedy deployment and immediate visibility. The self-auditing agent integrates with existing security solutions and respects current business processes.
- Reveal offers user training at the point of risk to help promote a security-conscious business culture. When users violate the data handling policy, an instructive message will advise the user of their error while the tool restricts the prohibited activity.
See Reveal in action for yourself; book a demo and discover how it can help your business protect its valuable data.
Frequently asked questions
Does behavioral analytics have applications outside of IT security?
Yes; some examples of industries employing behavioral analytics include:
- Marketing and advertising firms that make use of behavioral analytics to create more effective campaigns
- The gaming and entertainment industry where user behavior is closely analyzed in the pursuit of an enhanced gaming experience
- The healthcare sector where patient outcomes and behaviors are analyzed to develop more effective treatment plans
Why is behavioral analytics excellent at identifying insider threats?
Behavioral analytics can detect activities that typical users would not need to do in performing their jobs. While these actions may have a legitimate explanation, they may also be the signs of insiders attempting to exfiltrate data or engage in other malicious activities, thereby warranting investigation by security personnel.
Why is risk scoring an important feature of a behavioral analytics solution?
Risk scoring better helps the management of alert generation so the most impactful issues can be dealt with promptly. Not all anomalies require the same level of concern and should not be creating the same types of alerts. Without risk scoring, an IT support team may be subjected to alert overload and miss critical warnings.