Gravwell
Rethinking the SIEM for the Realities of Modern Enterprise Data

Corey Thuen, Founder and CEO

Enterprise security is only as strong as the breadth and integrity of the data it preserves. As cloud use expands, operational technology environments remain critical, and AI adoption introduces new operational layers; telemetry volumes have grown beyond what legacy tools and architectures were designed to handle.

Many traditional security information and event management (SIEM) platforms were built as analytics engines first and databases second—if at all. That design worked when data volumes were manageable; 500 gigabytes used to be considered ‘big data.’ Now, organizations are creating terabytes to petabytes of data every day. Despite leaps and bounds in security technology, legacy SIEMs and log management tools struggle to store such massive quantities of raw telemetry for extended periods while needing to maintain search performance and cost predictability.

Gravwell was built from the outset to address that structural limitation, posing the question, “What would a SIEM look like if built from scratch with the lessons learned over the past decade?” Positioned as a security data platform (SDP) rather than just a SIEM or detection engine, Gravwell is a time-series data lake with SIEM capabilities and analytics built on top, separating the function of storing ground-truth telemetry from the tools that analyze it to enable organizations to economically ingest and store all their data in full fidelity.

At the core of Gravwell’s architecture is a structure-on-query model. Instead of normalizing logs before ingestion, the platform preserves raw records, including binary data, NetFlow, full packet capture and even malformed logs, exactly as received. This model only applies structure at query time, enabling organizations to ingest data types that legacy SIEMs can’t because they parse data on ingest and force it into rigid schemas. This results in dropped data and blind spots across the organization where threats can go undetected.

The distinction becomes especially clear when systems change. For example, if a firewall vendor modifies a log format, schema-on-ingest systems can break, creating ingestion gaps until parsers are updated. With Gravwell’s structure-on-read, data preservation is the priority. Data collection continues, completely uninterrupted.

“One practical advantage is continuity,” says Corey Thuen, founder and CEO. “We continue collecting telemetry even when formats change or records arrive imperfectly. Raw data is power and potential. You might not know which questions you need to ask before you need to ask them.”

Thuen’s background in offensive security shaped this internal discipline, where he cultivated the philosophy of “collect absolutely everything.” He knows how important it is to have all data on hand during an investigation, because you don’t know which questions you need to ask during an incident and which data you’ll need.

“Sometimes it’s a single log entry that tells you how a bad guy got in,” he says.

Gravwell accepts data types that many platforms cannot ingest directly, including binary records and PCAP. That flexibility closes gaps created by tool limitations or format incompatibilities. It preserves what Thuen describes as the “defender’s advantage,” the ability to build organization-specific detections as opposed to relying solely on lowest-common-denominator templates.

The result is investigative continuity without silent data loss. Security teams can revisit historical data, apply new detection logic retroactively and reconstruct events months or years later without discovering that key telemetry was filtered out at ingest.

Scaling Data Without Scaling Cost

How does predictable pricing change security teams’ data collection and investigation behavior?

Scalability without affordability does not solve the enterprise problem. One of the primary reasons organizations limit telemetry collection is economic unpredictability.

Under ingestion-based pricing models, collecting additional logs or running high query volumes during incidents can trigger cost spikes. That creates financial pressure at the exact moment investigative intensity increases.

One practical advantage is continuity. We continue collecting telemetry even when formats change or records arrive imperfectly. Raw data is power and potential. You might not know which questions you need to ask before you need to ask them.

The economic impact is measurable. Organizations that switched from legacy platforms report average savings of roughly 40 percent while expanding data collection and retention. Teams that were previously limited to 90 days of searchable logs can now retain a full year of logs. Some maintain up to five years of searchable data without additional cost escalation.

Predictable pricing alters strategic behavior. Security teams investigate aggressively without worrying about overages. They collect broader telemetry sets and support additional departments using the same data service layer.

Mission Support: Structured Transition with No Disruption

Why is migration support critical when replacing legacy SIEM systems in active environments?

Mission Support is not just a built-in service; it's also a core philosophy. It is Gravwell’s dedicated customer success and transition function, which provides white-glove customer service from onboarding to success, and ongoing support that most SIEM vendors do not provide.

“Attackers won't slow down to help reduce stress during a switchover,” says Thuen. “That’s exactly why Mission Support matters. Customers need a white-glove experience because they can’t afford disruption, and it's a great opportunity to review and close visibility or detection gaps, which we love doing.”

Supporting Cloud and Isolated Environments Without Compromise

How does unified deployment support both cloud and air-gapped security environments effectively?

Security architecture breaks down when it assumes a uniform infrastructure. Power generation facilities, healthcare systems, and industrial control environments often operate in segmented or air-gapped networks where sending telemetry to a public cloud is not viable. At the same time, other parts of the organization may prefer SaaS deployment for operational simplicity.

Tools designed exclusively for one model force a trade-off. Either sensitive data is moved where it should not be, or visibility is fragmented across environments.

Gravwell’s architecture was built to eliminate that compromise. The same core platform runs in fully isolated deployments, hybrid environments and SaaS configurations without redesign. On-premises installations retain full functionality and search performance. SaaS deployments operate under the same structure-on-read model and pricing discipline.

That consistency has operational impact. Security teams should not maintain parallel tooling for different infrastructure segments. They should not lose visibility in air-gapped environments. They should not accept reduced capability to preserve sovereignty. The architecture allows organizations to operate under their own infrastructure constraints without sacrificing retention depth, search performance or cost predictability. Deployment model becomes a business decision, not a technical limitation.

Auditing AI and Preserving Ground Truth

Gravwell applies the same system-of-record discipline to AI-driven activity.

As organizations deploy automation and agent-driven workflows, the systems they deploy generate actions that must be traceable. Thuen points out that one of the emerging issues with AI is straightforward: “How do you audit what the AI did?”

Gravwell’s structure-on-read architecture ensures that telemetry generated by AI systems is retained in its original form, alongside traditional logs, network traffic and system events. The platform does not rely solely on summarized alerts or derived interpretations. It stores the underlying records and enables advanced correlation.

That design allows teams to reconstruct sequences of activity if something behaves unexpectedly. If an automated workflow modifies a configuration or triggers a downstream event, investigators can query the original data to understand what occurred and in what order.

Because Gravwell’s pricing model does not penalize additional searches or long-term retention, those records remain accessible without cost pressure. AI-driven activity can scale without creating blind spots or financial trade-offs.

In practice, Gravwell extends its system-of-record philosophy to automation. As new machine-generated telemetry appears, it is collected, preserved and made searchable under the same architecture that governs the rest of the environment.

Built for Scale from Inception

Gravwell’s architecture reflects the background of its founding team. With roots in supercomputing, it approached security data as a scale problem first. The platform has served as a system of record for the Supercomputing Conference, where sustained throughput and precision are non-negotiable.

That experience influenced how the database was designed. Instead of modifying a traditional SIEM to handle growing telemetry, Gravwell built its own data layer to support extreme ingestion rates, consistent search performance and predictable economics under load.

Those design decisions now define the platform’s enterprise positioning. As organizations expand hybrid infrastructure and deploy AI-driven systems that generate additional machine activity, Gravwell’s structure-on-read architecture and cost model remain stable. Data is retained without forced normalization. Query performance does not degrade as volumes increase. Pricing does not fluctuate based on search intensity.

Recognition as the Advanced Security Data Platform of the Year 2026 reflects that engineering foundation. Gravwell’s architecture was designed for scale from inception.

For enterprises evaluating long-term security architecture, that distinction is operational. A system of record must preserve raw telemetry reliably, remain searchable over extended retention periods and function predictably during incident spikes and investigative surges. Gravwell’s architecture is designed to meet those conditions consistently, positioning the platform as infrastructure beneath detection as well as a tool layered on top of it.