The Fingerprint of Normalcy: Establishing User Behavioral Baselines for Zero Trust (Activity 7.3.2)

We’ve invested in powerful analytics tools capable of sifting through massive volumes of security data from across our Zero Trust pillars (Activity 7.3.1). We’ve also begun analyzing logs for general user and device risk levels (Activity 7.1.3). Now, to detect subtle anomalies and sophisticated threats, we need to precisely define what “normal” looks like for every individual user. This process of empirically identifying typical user behavior patterns is the very essence of Zero Trust Activity 7.3.2: Establish User Baseline Behavior. The patterns and insights generated here will then directly inform the broader “User/Device Baselines” approach (Activity 7.2.5).

This activity focuses on the empirical process of building accurate behavioral baselines for our users. It mandates that DoD Components utilize the analytics tools implemented (from 7.3.1) to analyze subject (user) behavior patterns. The core goal is to identify patterns and deviations from normality, leveraging advanced techniques in analytics, specifically machine learning (ML) and User and Entity Behavior Analytics (UEBA). This moves beyond simple thresholds to statistical and algorithmic understanding of behavior, creating the living definition of “normal” for each user.

This activity is vital for proactively identifying compromised accounts, insider threats, or users operating outside their typical roles. By defining “normal” behavior with precision, any departure becomes a strong indicator of potential risk, enabling a proactive security posture.

The outcomes for Activity 7.3.2 highlight the analytical output:

1. Establish subject behavior patterns in order to differentiate normality/abnormality.
2. Identify opportunities for ML usage in analytics. (Beyond the initial baselining, finding new ways to apply ML for behavioral insights).

The ultimate end state underscores the strategic purpose: Patterns established will provide Components with decision making for user/device baselines. This feeds directly into subsequent activities that build comprehensive user and device profiles for dynamic access control.

Solutions for Achieving Establish User Baseline Behavior

Implementing Activity 7.3.2 requires configuring and leveraging your central analytics platform to collect, analyze, and model user behavior from diverse data sources, directly applying the approach defined in Activity 7.2.5:

1. Configuring Analytics Tools for User Baselining (Elastic’s Role):
   1. Your primary “analytics tool,” the Security Information and Event Management (SIEM) / Extended Detection and Response (XDR) platform (e.g., Elastic Security), is the central hub for this activity. It efficiently ingests and processes the massive amounts of standardized log data (from Activity 7.1.2) needed to build these baselines.
   2. Elastic Security is configured to apply its integrated UEBA capabilities, which utilize machine learning (ML) and statistical analysis to automatically identify typical patterns in user behavior from the ingested data over time. This operationalizes the “subject/attribute baseline approach” defined in 7.2.5.
   3. This process results in the actual “established subject behavior patterns” – the living definition of normalcy within the system.
2. Feeding Rich Identity Data for User Context (Okta’s Contribution):
   1. Okta, as your Enterprise Identity Provider, is a foundational data source for building accurate user baselines.
   2. Okta’s logs (e.g., successful/failed login attempts, Multi-Factor Authentication (MFA) challenges, applications accessed via SSO, administrative actions, login times, locations) provide direct insight into user identity and authentication behavior. This information, when fed into Elastic Security’s UEBA engine, allows it to build baselines like: “User X typically logs in from this country between 8 AM and 5 PM,” or “User Y rarely accesses application Z.”
   3. Okta’s attributes (roles, groups, department) are crucial for segmenting users, allowing for more accurate baselines (e.g., “normal behavior for a developer” vs. “normal behavior for an HR manager”).
3. Leveraging Endpoint Activity for Behavioral Patterns (Trellix’s Contribution):
   1. Trellix’s EDR/XDR platform provides rich, granular endpoint telemetry (process execution, file activity, network connections, application usage, command line details) that captures how users interact with their devices.
   2. This data, ingested by Elastic Security, is vital for understanding user behavior on devices. It allows baselines to be built around patterns like: “This user typically runs these applications,” or “This user doesn’t usually download large files to a USB drive.”
   3. While Activity 7.3.2 specifically focuses on user baselines, user activity inherently manifests on devices, making Trellix’s insights indispensable for a comprehensive user behavioral profile.
4. Implementing ML and UEBA Techniques:
   1. The analytics tool (Elastic Security) is configured to apply Machine Learning (ML) algorithms to the collected data. These algorithms automatically learn typical patterns and deviations without requiring manual rule creation for every anomaly.
   2. The UEBA component builds statistical models and behavioral profiles for each user, actively differentiating normal from abnormal activities. This also includes identifying opportunities for further ML application to refine baselines or detect new types of anomalies.

Key Items to Consider:

• Reliance on 7.2.5 Methodology: The success of this activity hinges on the clear and accurate baseline approach defined in Activity 7.2.5.
• Defining “Normal” for Diversity: Accurately defining what constitutes “normal” behavior for a highly diverse user base (different roles, departments, locations, access patterns) is complex. Baselines must be segmented appropriately.
• Data Volume and Historical Context: Building robust baselines requires collecting and storing massive amounts of high-quality, historical log data. Data pipeline optimization (Activity 7.1.2) is critical here.
• Managing Seasonality and Evolution: User behavior evolves (e.g., seasonal peaks, new applications, remote work trends). Baselines need to be continuously updated and refined through ongoing ML model training to remain accurate and minimize false positives.
• False Positive Management: Behavioral anomalies can generate many false positives initially. A robust process for analyst review and feedback (human-in-the-loop) is essential for tuning.
• Attribute Selection: Carefully select the most relevant attributes to baseline. Too many can create noise; too few can miss important anomalies.
• Privacy Considerations: Especially for personal devices (BYOD) or highly sensitive roles, careful consideration of user privacy and data collection policies is necessary when building behavioral baselines.
• Integration with Policy: The insights from these baselines (e.g., a “risk score”) must be consumable by policy enforcement points to trigger dynamic access decisions.

For the Technical Buyer

Activity 7.3.2 is where you truly operationalize behavioral analytics for your Zero Trust architecture. It’s about taking the baseline methodology defined in Activity 7.2.5 and actively building accurate user behavioral baselines using your analytics tools. For technical buyers, success here means leveraging your Elastic Security platform’s powerful analytics and UEBA capabilities, fueled by the essential identity context from Okta and the rich endpoint telemetry from Trellix. This integrated approach allows you to build comprehensive user behavioral baselines, which are indispensable for automatically identifying deviations and anomalies that signal advanced threats, compromised accounts, or insider risks. This activity ensures your Zero Trust security posture is continuously informed by the real-time patterns of your digital landscape, enabling more precise detection and response.

Pillar: Visibility & Analytics 

Capability: 7.3 Common Security and Risk Analytics

Activity: 7.3.2 Establish User Baseline Behavior

Phase: Target Level 

Predecessor(s): 1.6.1 Implement User & Entity Behavior Activity (UEBA) and User Activity Monitoring (UAM) Tooling, 7.1.3 Log Analysis

Successor(s): 7.2.5 User/Device Baselines; 7.4.1 Baseline & Risk Profiling Pt1