The Reveal Platform
Legacy SIEMs often force a trade-off between investigative depth and analytical speed. Gurucul REVEAL eliminates that compromise by using a schema-on-write architecture, delivering both the verifiable evidence of raw logs and the high-velocity precision of normalized data—so your AI-driven detections are never a “black box.”
When organizations evaluate a SIEM, they typically focus on detection capabilities, AI, and automation. But beneath these advanced features lies a fundamental question: Can I trust and understand the data my SIEM is using?
For security teams—especially those leveraging UEBA-driven threat detection—the ability to work with both raw and normalized data is critical. Gurucul delivers both without forcing you to choose.
Most organizations evaluating a modern SIEM prioritize detection logic and automation, yet they overlook the data foundation that underpins those features. Legacy platforms often rely on schema-on-read, ingesting a “swamp” of unstructured logs and attempting to make sense of them only when a query is run. When an analyst investigates a high-priority alert, the system must parse and normalize disparate fields such as src_ip, client_ip, and connection_source in real time across billions of rows. The result? Significant processing delays, fragmented visibility, and “invisible fields” that were never indexed adequately during ingestion.
To achieve true Radical Clarity, a SecOps platform must deliver two distinct types of data simultaneously:
Raw Data is the original, unaltered event log from your cloud, identity, or network sources. It serves as the definitive evidence for compliance audits, troubleshooting ingestion errors, and validating that a detection wasn’t a “hallucination”. When an alert fires, security teams need more than “something happened”—they need to know why, and raw data delivers that certainty.
Normalized Data is the foundation for AI/ML analytics and UEBA. Every system logs data differently—one uses src_ip, another client_ip, and another embeds it in a message string. Searching across these variations is inefficient.
By mapping disparate log formats into a standard schema at ingestion (schema-on-write), behavioral models can operate on consistent, comparable data. This is the fuel for advanced UEBA and Insider Threat models, enabling cross-domain correlation between a suspicious Okta login and an anomalous AWS S3 bucket download. Without this dual-stream approach, your AI is either a “black box” of unverifiable analytics or a swamp of unstructured logs that no analyst can search effectively.
As the foundation for data science, normalization ensures that behavioral models and advanced analytics can operate on consistent, comparable data. Without it, your SIEM becomes fragmented, and machine learning models fail to deliver accurate insights.
Normalization delivers powerful benefits: it enables seamless correlation across identity, endpoint, cloud, and SaaS platforms; ensures consistent detections across diverse vendors; supports scalable analytics as new data sources are added; and powers advanced UEBA and Insider Threat models. Simply put, normalization is the foundation for accurate, efficient, and future-ready security analytics.
Schema-on-read vendors add significant time and complexity to basic analytics because they must interpret and normalize data at query time. Gurucul eliminates that overhead, enabling real-time insights and faster investigations. Unlike traditional vendors that rely on schema-on-read, Gurucul uses schema-on-write. This means
Gurucul REVEAL eliminates the trade-off between investigative depth and analytical precision. Our platform leverages an Intelligent Data Fabric that performs normalization, enrichment, and risk scoring in real time as data enters the system. Unlike platforms that enforce a rigid, proprietary schema, Gurucul’s schema-independent architecture allows normalized data to align with OCSF, existing vendor schemas, or customer-defined models—without compromising analytics or investigation workflows.
Gurucul eliminates that overhead, enabling real-time insights and faster investigations. Gurucul helps customers gain control over normalization:
For example, Gurucul can map multiple identity systems to a single employee ID, normalize cloud-specific fields alongside on-premises logs, and create custom business attributes to accelerate investigations. The result is cleaner data, better analytics, and quicker response times—plus the ability for analysts to pivot instantly to raw logs retained side by side for validation.
Gurucul believes in data democracy—ensuring transparent, consistent access to normalized and raw data without black-box abstractions, regardless of how or where the data is stored. Analysts can move freely between normalized insights and raw evidence, enabling faster investigations, greater confidence, and better outcomes, with a consistent experience regardless of the customer’s chosen data lake or storage architecture. For organizations evaluating SIEM and UEBA platforms, Gurucul delivers:
Bottom Line: Modern security operations demand clarity, speed, and confidence. Raw data delivers truth and traceability, while normalized data provides consistency and actionable intelligence. Without both, your SIEM risks becoming either a black box of analytics or a swamp of unstructured logs. Gurucul bridges this gap — delivering trustworthy detections backed by verifiable evidence, accelerating investigations with unified, searchable data, and scaling effortlessly as new sources are added. In short, Gurucul combines high-level analytics with detailed raw evidence, providing UEBA insights and investigative depth without compromise.
Stop guessing. Start verifying. Explore Gurucul REVEAL and see how schema-on-write transforms security operations—request a live sandbox demo today.
Contributors:
Habeeb A.
