Solving the Normalization Problem in Security Operations

Security teams today face a data management crisis. Logs, alerts, and telemetry come from every layer of the enterprise—from endpoints and firewalls to SaaS applications and cloud platforms. Each source speaks its own language, creating fragmentation and forcing teams to rely on normalization to make sense of it all. But normalization adds complexity, delays detection, and creates blind spots through schema drift. The result is a SOC struggling to manage both scale and accuracy.

Customer Challenges

Data Fragmentation and Schema Drift
Security data lives everywhere, often in inconsistent formats. Every time a vendor updates a log schema, mappings break and context is lost. Analysts are left chasing missing fields or outdated connectors instead of investigating threats.
Analyst Fatigue and Overload
With alert volumes skyrocketing, analysts spend more time translating data than interpreting it. Each system uses a different query language, adding cognitive load and training overhead that slow investigations.
Inefficiency and Rising Costs
The normalization process consumes storage, compute, and staff time. It’s a hidden tax on every investigation and an obstacle to scaling security operations efficiently.

The Solution: Query Without Normalization

Crogl’s patented technology eliminates the need to normalize data before analysis. Instead, it understands fields dynamically—recognizing and aligning identifiers such as IP addresses or user IDs across disparate systems automatically. This approach allows analysts to search and correlate across all data sources without schema dependencies or rigid mapping.

Business Impact

Faster Investigation and Response: By removing normalization steps, investigations start instantly with full context and history available in one place.
Reduced Training Requirements: Analysts no longer need to master multiple query languages or maintain fragile connectors.
Operational Efficiency: Fewer FTEs are required to manage data pipelines, freeing resources for proactive threat hunting.
Improved Accuracy: Eliminating schema drift ensures that updates or changes in log formats never obscure critical signals.
Empowered Analysts: Rather than replacing analysts, automation enhances their visibility and decision-making—turning every investigation into a learning opportunity.

Outcome

This shift redefines how SOC teams interact with data. Instead of normalizing and indexing, they query directly and intelligently. By addressing the root cause of inefficiency—data friction—Crogl enables a more resilient, scalable, and human-centered approach to security operations.

Learn more about CROGL: https://itspm.ag/crogl-103909

Note: This story contains promotional content. Learn more.

Cory Wallace, Director of Product Marketing at CROGL
On LinkedIn: https://www.linkedin.com/in/corywallacecrogl/

Learn more and catch more stories from CROGL: https://www.itspmagazine.com/directory/crogl

Technical capabilities Discussed

Search across unnormalized data
Ability to run queries directly on raw, unstructured, and inconsistent data without forcing it into a schema.
Automatic field recognition across systems
IP addresses, user IDs, and other identifiers are matched and unified even when naming conventions differ across platforms.
Schema drift immunity
Log format changes from vendors no longer break pipelines or cause missing fields during investigations.
Cross-platform query generation
The system automatically creates and runs queries across environments that use SPL, KQL, SQL, and other query languages.
Integrated kill chain investigation steps
Background automation that pre-populates investigation context using the full kill chain, reducing manual hops across systems.
Unified evidence chain assembly
Analysts receive correlational context and data pulled from multiple storage layers (SIEM, lake, cloud buckets) in a single view.
Alert enrichment before analyst review
The system gathers historical context and data relationships before the analyst opens the ticket.
Direct access to data stored across lakes and cloud buckets
Investigation does not require copying or re-indexing the data first.
Analyst skill amplification without extra tools
Analysts get to see the generated queries and evidence steps, improving investigation skill development.
Reduced dependency on normalization pipelines
No need for custom connectors, mapping scripts, or data transformation layers.