Log Analysis for User and Device Risk - Zero Trust Activity 7.1.3

Unveiling Behavioral Insights: Leveraging Log Analysis for User and Device Risk (Activity 7.1.3)

We’ve worked hard to establish a robust framework for collecting and standardizing our security logs (Activity 7.1.2). Now, we move beyond mere collection to the crucial phase of analysis, extracting actionable intelligence from this data to understand user and device behavior and assess their risk. This brings us to Zero Trust Activity 7.1.3: Log Analysis.

This activity is about transforming raw log data into meaningful insights that directly feed your Zero Trust decision-making. The Enterprise initiates this by developing common user and device activities, defining what “normal” behavior looks like across the organization. Components then identify and prioritize these activities based on risk. The initial focus is on events and flows deemed the most simplistic and risky, for which analytics are created using different data sources, such as logs. Over longer periods, trends and patterns are developed, moving beyond point-in-time checks to understand dynamic behavior.

This activity is vital for providing continuous risk scores for users and devices, enabling truly adaptive access policies. By understanding deviations from normal patterns, you can detect anomalous and potentially malicious behavior that might bypass traditional, static security controls.

The outcomes for Activity 7.1.3 highlight the analytical progression:

Identify activities to analyze.
Determine risk level per events/flows.

The ultimate end state underscores the continuous security benefit: Components utilize logs to develop risk level for each user and device. This operationalizes risk assessment as a core component of your Zero Trust strategy.

Solutions for Achieving Log Analysis

Implementing Activity 7.1.3 requires robust analytical platforms and a systematic approach to defining and monitoring user/device behavior:

Defining Common User and Device Activities (Governance):
1. Process: The Enterprise, collaborating with Components, defines a standardized taxonomy of common user and device activities (e.g., “typical login pattern,” “standard application access,” “normal data transfer volume for role X”). This creates the baseline for behavioral analysis.
2. Risk Prioritization: Components prioritize these activities based on their associated risk. “Simplistic and risky” events (e.g., failed logins from new locations, unusual port scanning, access to highly sensitive applications) are targeted first for analytics development.
Leveraging SIEM/XDR with UEBA Capabilities:
1. Central Analytics Platform: Your Security Information and Event Management (SIEM) / Extended Detection and Response (XDR) platform (e.g., Elastic Security) is the core technology for this activity. It ingests the standardized logs (from Activity 7.1.2) and performs the necessary analysis.
2. Behavioral Baselines: Utilize the UEBA (User and Entity Behavior Analytics) capabilities within your SIEM/XDR (or integrate with dedicated UEBA solutions) to establish baselines of normal behavior for each user and device across various activities. This requires analyzing historical data over longer periods to identify trends and patterns.
3. Anomaly Detection: Configure analytics rules (often driven by machine learning) to detect deviations from these baselines. These anomalies indicate “risky events/flows.”
Integrating Diverse Data Sources for Analytics:
1. Logs as Foundation: Leverage the standardized logs from all sources (firewalls, EDR/XDR, identity systems, network devices, applications, cloud services) to fuel your analytics.
2. User and Device Context: Combine log data with rich context from your IdP (user attributes, roles), UEDM (device management status, compliance), and ITAM/CMDB (asset criticality) to enrich the events and support risk determination.

How Cribl and Elastic Work Together to Achieve Desired Outcomes:

Your strategic choices of Cribl for data optimization and Elastic for central analysis are crucial for ensuring the effectiveness and accuracy of your log analysis in Activity 7.1.3:

Cribl (The Data Optimizer for Analytics):
- Ensuring Data Quality for Analysis: Cribl Stream plays a crucial role by optimizing and standardizing logs before they reach Elastic Security for analysis. It ensures that the vast volume of logs from sources like Zscaler (for network access), Okta (for identity), and Trellix (for endpoint) is cleaned, normalized, and consistently formatted. This high-quality data is absolutely essential for the accuracy of behavioral analytics and for developing reliable “trends and patterns” over longer periods in Elastic.
- Managing Scale for Trends: By intelligently filtering and routing data, Cribl helps manage the massive data volumes required for long-term trend analysis without overwhelming Elastic Security, making it feasible to develop robust baselines.
- Enrichment-in-Transit: Cribl can also perform initial data enrichment before ingestion, adding valuable context to logs that further enhances Elastic Security’s analytical capabilities.
Elastic Security (The Receiver, Analyzer, and Risk Scorer):
- Central Analytics Engine: Elastic Security serves as the primary platform where the comprehensive analysis and behavioral profiling occur. It efficiently ingests the high-quality, standardized data prepared by Cribl.
- Building Baselines & Detecting Anomalies: Leveraging its powerful analytics and UEBA capabilities, Elastic Security builds intricate baselines of normal user and device behavior. It then automatically flags deviations, identifying “risky events/flows” and “advanced threat events.”
- Developing Risk Levels: Elastic Security can use these analytics to calculate and assign dynamic risk levels to individual users and devices, achieving the activity’s end state. This real-time risk scoring is fundamental for driving adaptive access policies in Zero Trust.
The Combined Synergy: Cribl ensures that the vast and diverse log data from across your enterprise is delivered to Elastic Security in the highest quality, in a standardized format, and with optimized volume. This powerful combination makes Elastic Security far more effective as your central analytics platform, enabling it to focus its resources on advanced analysis, behavioral profiling, and risk scoring, rather than on data wrangling.

Key Items to Consider:

Defining “Common Activities”: This is a governance challenge. It requires collaboration between security, IT, and business units to accurately define normal behavior for different roles and device types.
Data Volume and Retention for Trends: Developing accurate trends and patterns requires collecting and storing large volumes of historical log data. Plan for sufficient storage and processing capacity.
Managing False Positives: Behavioral analytics can generate alerts on legitimate but unusual activity. A robust process for tuning baselines and triaging alerts is essential to minimize false positives.
Continuous Refinement: Baselines and analytics rules need continuous refinement as user behaviors, IT environments, and threats evolve.
Risk Model Integration: The output of log analysis (risk levels per user/device) must be integrated into your policy enforcement points (e.g., ZTNA, access gateways) for dynamic access decisions.

For the Technical Buyer

Activity 7.1.3 is about unlocking the intelligence hidden within your log data, transforming it into actionable insights about user and device risk. For technical buyers, this means leveraging your SIEM/XDR platform (e.g., Elastic Security) to build robust behavioral baselines and detect anomalies, a process heavily reliant on high-quality, standardized logs. Cribl acts as your data optimization layer, ensuring that this vast amount of log data is cleaned, normalized, and delivered efficiently for accurate analysis. This combined power enables you to develop continuous risk levels for every user and device, providing the dynamic intelligence needed for truly adaptive access controls and a proactive security posture within your Zero Trust architecture.

Pillar: Visibility & Analytics

Capability: 7.1 Log All Traffic

Activity: 7.1.3 Log Analysis

Phase: Target Level

Predecessor(s): None

Successor(s):