Standardizing Log Data for Visibility - Zero Trust Activity 7.1.2

Standardizing Log Data for Enterprise-Wide Visibility: Log Parsing (Activity 7.1.2)

We’ve established the critical need for scalable monitoring and detection in Zero Trust, recognizing that security data volumes are immense (Activity 7.1.1). However, raw security logs from disparate sources often have disparate formats and are not standardized, making correlation and analysis a nightmare. This challenge brings us to Zero Trust Activity 7.1.2: Log Parsing.

This activity is about bringing order and clarity to security telemetry. It mandates that DoD Components identify and prioritize log and flow sources (e.g., firewalls, Endpoint Detection & Response, Active Directory, switches, routers, etc.). A systematic plan for collection of high-priority logs first, then low-priority, is developed. The heart of this standardization effort is that an open industry-standard log format is agreed upon at the DoD Enterprise level with the Components, and implemented in future procurement requirements. Finally, existing solutions and technologies are continually migrated to this format.

This activity is vital for ensuring that your SIEM (Security Information and Event Management) system receives consistent, high-quality, and digestible data, which is essential for effective detection, investigation, and automated response within your Zero Trust architecture.

The outcomes for Activity 7.1.2 highlight this critical standardization:

Enterprise standardized log formats.

Components implement rules developed for each log format.

Solutions for Achieving Log Parsing

Implementing Activity 7.1.2 requires a structured approach to log management, focusing on prioritization, standardization, and automated ingestion into your SIEM:

Log Source Identification and Prioritization:
- Process: Components conduct a comprehensive audit of all log and flow generating sources across their environments. This includes network devices, endpoints, servers, applications, cloud services, and identity systems.
- Prioritization: Categorize logs by criticality to Zero Trust policy enforcement, threat detection, and compliance requirements. High-priority logs (e.g., authentication logs, EDR alerts, firewall denials, privileged access events) should be collected first.
Defining and Adopting an Enterprise Standard Log Format:
- Process: The Enterprise, in collaboration with Components, selects an open industry-standard log format. This format dictates the schema, field names, and data types for common log events. This ensures consistency across diverse sources.
- Examples of Open Industry Standards:
  - Common Event Format (CEF): A widely adopted text-based format for exchanging security information from various devices and applications.
  - Elastic Common Schema (ECS): A popular open standard for structuring data ingested into the Elastic Stack, providing a consistent way to organize security events.
- Procurement Requirement: Integrate this standard into future procurement requirements (as noted in Activity 2.5.1 for APIs), ensuring new solutions inherently support the chosen format.
Implementing Log Collection and Migration:
- Parsing and Normalization (Cribl’s Contribution): This is where tools like Cribl Stream become invaluable. Instead of performing complex parsing directly at the SIEM, Cribl acts as a powerful data pipeline tool that can ingest raw logs from various sources (e.g., firewalls, endpoints, cloud services, and even Zscaler’s cloud platform). It provides a flexible environment to filter, parse, normalize, and transform these diverse logs into the agreed-upon enterprise standard format (e.g., ECS, CEF) before they are forwarded to the SIEM. This ensures consistency and optimizes the data quality for downstream analytics.
- Continuous Migration: Establish a continuous process to migrate existing solutions and technologies to generate or convert their logs into the new standard format, prioritizing high-volume and high-value sources.
Filtering and Forwarding to SIEM:
- Implement intelligent filtering, often within data pipeline tools like Cribl, to reduce noise and forward only relevant, high-fidelity, and now standardized log events to the SIEM. This optimizes SIEM performance and reduces storage costs.

How Cribl and Elastic Work Together to Achieve Desired Outcomes:

Your strategic choices of Cribl for log optimization and Elastic for central analysis are crucial for Activity 7.1.2:

Cribl (Data Pipeline & Log Standardization Orchestrator): The “Translator” and “Optimizer.”
- Log Ingestion & Aggregation: Cribl can efficiently ingest logs from a vast array of sources across your enterprise (including high-priority data from platforms like Zscaler for network access, and Okta for identity).
- Real-time Parsing & Normalization: Cribl is ideally positioned to take these raw logs and dynamically parse, enrich, and normalize them into your enterprise’s agreed-upon standard log format (e.g., ECS, CEF). It effectively “translates” the different log languages into one unified format, which is essential for consistent analysis by Elastic Security.
- Intelligent Filtering & Routing: Cribl allows you to apply intelligent filters to reduce log volume, ensuring only relevant, high-fidelity data is forwarded to the SIEM, optimizing ingestion costs and performance in Elastic.
Elastic Security (Central SIEM & Analytics): The “Receiver” and “Analyzer.”
- Central Destination: Elastic Security, acting as your SIEM, is the central destination for all your standardized log data. It’s designed to efficiently ingest, store, and analyze this pre-processed data.
- Efficient Analysis: Because Cribl has already transformed logs into a consistent format, Elastic can ingest and analyze them much more efficiently, reducing the need for complex parsing rules within the SIEM itself. This allows Elastic to focus its resources on correlation, threat detection, and analytics.
- Comprehensive View: Elastic then provides the comprehensive view by correlating these standardized logs from all sources, enabling better detection of advanced threats that span multiple security domains.
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 SIEM, enabling it to focus its resources on advanced analysis, threat detection, and incident response, rather than on data wrangling.

Key Items to Consider:

Consensus on Standard: Gaining enterprise-wide agreement on a single open industry-standard log format is a significant governance challenge.
Data Pipeline Efficiency: Prioritizing log collection and implementing intelligent filtering and transformation (e.g., with Cribl) is crucial to avoid overwhelming your SIEM and managing costs.
Parsing Complexity: Even with a standard, the initial effort to create parsing rules for various raw sources into that standard format can be complex and resource-intensive.
Continuous Migration Effort: Migrating existing solutions to a new log format is an ongoing process that requires planning and resources.
Quality Control: Implement robust quality control checks to ensure logs are accurately parsed and conform to the standard before ingestion into the SIEM.

For the Technical Buyer

Activity 7.1.2 is about bringing order to the chaos of disparate log formats by establishing and enforcing an enterprise-wide standard. For technical buyers, success here means contributing to the decision of selecting an open industry-standard log format, then implementing robust log collection and parsing mechanisms, perhaps leveraging a powerful data pipeline tool like Cribl. This new standardization simplifies your SIEM operations, enhances data quality for advanced analytics, and is indispensable for achieving the correlation needed to detect and respond to threats effectively within your Zero Trust architecture.

Pillar: Visibility & Analytics

Capability: 7.1 Log All Traffic

Activity: 7.1.2 Log Parsing

Phase: Target Level

Predecessor(s): None

Successor(s):