The Five Stages of Effective UEBA

Artificial intelligence (AI) and machine learning (ML) technologies have dramatically improved in recent years. From chatbots such as ChatGPT, to self-driving cars, to facial recognition technology, AI has been transformed, almost overnight, from a sci-fi trope to a routine part of our daily lives. 

However, AI and ML technologies have many uses beyond the obvious. While AI’s more fantastic capabilities get the most publicity, its behind-the-scenes abilities are where AI shines. A notable example of this is in user entity and behavior analysis (UEBA). UEBA solutions use AI and ML to detect user, router, server, and endpoint anomalies in a network to prevent a wide range of cyber threats. Experts hold UEBA in such high regard that many regulatory standards – NIST, HIPAA, and PCI DSS, for example – require organizations to implement UEBA to comply.  

UEBA differs from traditional behavior monitoring in that it also monitors machines. Behavior monitoring solutions are helpful for insider threat management, but UEBA solutions can detect internal and external threats. For example, if one of an organization’s servers receives thousands more requests in a day than is typical, UEBA solutions would flag this as a possible Distributed Denial of Service (DDoS) attack.  

This article will outline the five stages of effective UEBA to help organizations implement it successfully.  

Stage One: Planning and Preparation 

Before implementing a UEBA solution, organizations must define their goals and objectives, assess their security needs and infrastructure, and identify the data sources they will use for monitoring user and entity behavior. Possible data sources include but are not limited to:  

• Network devices  
• Endpoints 
• Applications 

Organizations must also set up the necessary infrastructure, deploy data collection agents or sensors, and establish policies and procedures, for example.  

Stage Two: Data Collection and Integration 

Organizations must then collect and integrate relevant data from the above sources. This data can include but is not limited to:  

• Authentication logs 
• Web proxy logs  
• Email logs 
• VPN logs  
• Resource access logs 
• SAP Security Audit logs 

Organizations should then consolidate and normalize this data to provide a unified view for security teams to analyze.  

Stage Three: Data Analysis and Modeling 

Once an organization has collected and integrated the necessary data, it must analyze and model user and entity behavior. It must define normal behavior, establish baselines, and create models to detect anomalies or suspicious behavior. UEBA solutions typically employ advanced analytics techniques, such as machine learning algorithms, statistical analysis, and pattern recognition, to identify potential threats or suspicious activity.  

Employee behavior profiles are a typical result of data analysis and modeling processes. Behavior profiles establish the typical actions an employee takes in their day-to-day tasks. Suppose there’s a change in this behavior. In that case, UEBA solutions compare activity to the typical behavior of the employee’s peers and known insider threat patterns before alerting security teams if it identifies any red flags.  

Stage Four: Alert Generation and Response  

UEBA solutions then use the analyzed data to detect abnormal behavior and generate security alerts or notifications. Security analysts or administrators typically receive these alerts to investigate them further and take appropriate actions to respond to potential security incidents. Typical responses include but are not limited to:  

• Additional monitoring 
• User education 
• Tightening access controls  
• Initiating incident response procedures  

UEBA solutions, however, aren’t merely reactive; they can provide security teams with early warnings of malicious intent. By detecting early signs of malicious intent, such as an employee working from home out of hours for no apparent reason, accessing resources irrelevant to their role, or connecting various USB devices, UEBA solutions allow security teams to investigate possible threats before they come to fruition.  

It’s also crucial that security teams thoughtfully interpret UEBA solutions results; false positives could result in unnecessary investigations and disgruntled employees. Similarly, security teams should conduct additional analysis of alerts; if an employee consistently breaks cybersecurity policy, UEBA solutions will eventually view this behavior as usual and fail to issue a warning.  

Stage Five: Continuous Improvement and Refinement 

It’s essential to remember that UEBA implementation is not a “set-it-and-forget-it” process; it requires continuous improvement and refinement. Organizations should monitor the effectiveness of their UEBA solution, fine-tune their models and algorithms according to feedback and evolving threats, and adapt systems to changes in organizational infrastructure or security requirements. Organizations can optimize their effectiveness and enhance threat detection capabilities through regular review and analysis of system performance.  

UEBA solutions are essential for organizations seeking to protect themselves from internal and external threats. Through thoughtful, methodical, and tailored implementation, organizations can reduce the risk of a wide range of cybersecurity threats, including:  

• Fraud 
• Data exfiltration 
• Intellectual property theft 
• Espionage  
• Privilege abuse  

However, organizations must remember that the above implementation stages are not a one size fits all framework. To truly reap the benefits of UEBA solutions, organizations must tailor their approach according to their specific implementation and organizational requirements.  

 Josh is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He’s written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy.