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Cribl and Auguria:
Similarities and Differences

How do Cribl and Auguria differ?

In a nutshell: Cribl is a general-purpose data engineering tool that requires high levels of expertise and manual tuning to gain value. Auguria is a security operations (SecOps) tool that automatically, and without rules and extensive manual tuning, brings meaning and control to the glut of enterprise event data through the technological unification of machine intelligence and human security expertise.

Dig Deeper +

At Auguria, we are often asked, “how are you different from Cribl.” Fantastic question and one that we aim to address here. At the highest level, we posit that Cribl is a data engineering tool for generalized observability data flows and requires operators to be highly technical and adept at complex rule writing in order to get the most out of the Cribl platform. Auguria, while also a data engineering tool, contrasts with its focus on security operations (SecOps) data and workflows.

The biggest difference between Auguria and Cribl’s approaches is the ease at which Auguria helps SecOps get at the data that matters. What do you mean data that matters? Another great question. Data that matters is data that moves the needle with regard to decision making. It’s the data that’s left over after you’ve ruled out all other data that does not contribute to an analyst’s understanding of the challenges at hand. In Auguria’s terms, data that matters is defining everything that is “normal” then setting it to the side so that you can focus on the remainder which by definition is everything that is “not normal, abnormal, outliers.”

At the highest level, Auguria’s data science engine automatically, and without rules, transforms terabytes of bloated, noisy security data into actionable signal. The Auguria SKL™ transforms complex security data into usable knowledge through automated data collection, normalization, enrichment, and prioritization. One critical way Auguria differentiates from Cribl is in its automation of data compaction using data science functions instead of Cribl’s approach which requires analysts to figure out what questions to ask of the data, then apply these filters, tune these filters, and maintain these filters over time. Auguria is not just about data compaction though. When you take a closer look, analysts gain valuable insight about atypical outliers buried in the data haystack, what they are, and why they’ve surfaced for inspection.

What customers can expect from using Auguria’s Security Knowledge Layer

Event: “c:\program files (x86)\microsoft\edge\application\msedge.exe” –type=renderer–lang-en-us_ category: application, browser, edge total cut: 34,248,666 uniquecnt: 690,899
Event: “c:\program files (x86)\microsoft\edge\application\msedge.exe” –type=renderer–lang-en-us_ category: application, browser, edge total cut: 34,248,666 uniquecnt: 690,899
Event: “c:\program files (x86)\microsoft\edge\application\msedge.exe” –type=renderer–lang-en-us_ category: application, browser, edge total cut: 34,248,666 uniquecnt: 690,899

Auguria and Cribl: The Breakdown

Auguria and Cribl both offer data stream processing capabilities. Cribl focuses on log aggregation and transformation for observability, while Auguria also offers ingest aggregation skewing towards security analytics for threat detection and incident response on top of basic data processing.

Major Use Case Observability and Data Engineering Cybersecurity Operations
Users IT Operations SOC Teams
Data Integration and Transformation Capabilities (ETL)
Data Collection
Data Transformation
Data Routing
Event Enrichment
Rule-Based
AI-driven + Rule-based
Use Cases
SIEM Cost Optimization
Manual
Automatable
ETL for Data Lakes
Automated Incident Triage
Guided Threat Hunting
AI-driven Forensic Analysis
RAG for Security AI Assistant and Copilot
Integrated Data Architecture
Data Lake
Vector Database
Security Knowledge Engineering
Vector Embedding Engine
Customizable Ontology
Semantic Analysis and Filtering
Alert Prioritization and Ranking
Event Classification Engine
Exploratory Data Visualization

Auguria and Cribl in Brief

Reduce Data Volume

Focus

Tailor Implementations

Core Technology

No playbooks, no rules

Data Integration

Accelerate Threat Detection

Security Focus

Contextualize Intelligence

Data Processing

Eliminate False Positives

User Interface

Optimize Costs

Deployment Options

Bridge Skill Gaps

Ideal Customers

4 reasons why Auguria is better for security than a general-purpose observability tool (like Cribl)

Purpose-Built for Security Teams:

Auguria is specifically built to enrich the data relied upon by security operations and by doing so radically evolves a team’s ability to focus on exactly what’s important within that data.

Security-ready Advanced Machine Learning Capabilities:

Auguria leverages advanced machine learning techniques with native vector embeddings to perform deep semantic analysis. This sophisticated approach allows for a nuanced understanding of data, enabling the detection of complex, multi-layered and multi-stage security threats. These capabilities are crucial for adapting to new and evolving cybersecurity threats, ensuring your organization always remains one step ahead of attackers.

Optimized for SOC Efficiency:

Auguria radically improves the productivity of security teams by streamlining operations, driving faster response times and enabling more accurate threat response, as opposed to broad observability technologies that may not be optimized for security processes and are manual in nature.

Customizable Security Framework:

Auguria integrates a comprehensive Security Knowledge Model™ to ground threat contextualization and understanding with an intrinsic understanding of security concepts.

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