Real-Time Streaming Analytics
Telemetry is evaluated the moment it arrives. No scheduled queries, no dashboard-triggered correlation, no batch jobs. State is maintained continuously—not reconstructed from a database.
How We Evaluate a Next-Generation SIEM
A Next-Generation SIEM is defined by its architecture—not by dashboards or AI marketing. These criteria reflect how a SIEM ingests telemetry, creates context, and controls cost at modern scale.
What Defines a Next-Generation SIEM
A NG-SIEM is judged on how telemetry flows, how behavior is evaluated, and how the system reduces analyst toil. Each of the six criteria below is mandatory. Failing any of them means a platform remains a traditional SIEM regardless of branding.
Behavioral and Stateful Detection
Detection relies on behavior: seasonality models, clustering, threshold deviation, first-occurrence analysis, and state transitions—not piles of static rules.
Identity-First Correlation
Every event attaches to a person, device, application, or service. Identity becomes the organizing lens for understanding impact, scope, and relationships.
Vendor Capability Notes
Fluency — Native identity graph links people, devices, applications, and services; cases pivot around identity automatically.
Microsoft Sentinel — IdentityInfo schema unifies Microsoft telemetry, yet multi-vendor relationships still demand manual stitching.
Securonix EON — IAM/IGA integrations add rich identity context, but full identity graphing depends on services and tuning.
CrowdStrike Falcon SIEM — Identity-driven cases materialize when Identity Protection modules are licensed; coverage narrows beyond the Falcon stack.
Splunk Enterprise Security — Identity and asset lookups exist, though analysts maintain them manually through lookups and apps.
Google Chronicle — Maps IAM logs into user entities, but lacks a native identity-first investigation workflow.
Case-Based Investigation Workflow
Cases construct themselves automatically as the system accumulates evidence. Analysts review the narrative instead of assembling it manually.
Vendor Capability Notes
Fluency — Streaming case engine maintains evidence, timelines, and lifecycle without analyst assembly.
Microsoft Sentinel — Incidents group alerts automatically, yet analysts architect narrative and correlation manually.
Securonix EON — Risk-based incidents form cases with enrichment, though workflows stay analyst-directed.
CrowdStrike Falcon SIEM — Identity-driven incidents excel within Falcon; automation drops off for third-party telemetry.
Splunk Enterprise Security — Episode Review groups notables but relies on search-driven manual evidence gathering.
Google Chronicle — Investigation timelines exist, yet there is no native case object or lifecycle.
Noise Reduction Through Event Hierarchy
Raw telemetry collapses into contextual signals via scoring, clustering, deduplication, enrichment, and grouping so the SIEM explains the environment instead of overwhelming it.
Streaming Data Fabric
The SIEM sits atop a streaming data fabric that collects telemetry, parses and enriches inline, filters noise, and routes data to SIEM, lake, or archive tiers—supporting Parquet/object storage and separating analytics workload from storage cost.
Vendor Capability Notes
Fluency — Full streaming fabric with native filtering, routing, enrichment, normalization, and Parquet/object placement.
Splunk — Requires Cribl or third-party fabric for routing/filtering; native product is index-first.
Microsoft Sentinel — Event Hub ingestion lacks inline filtering, routing logic, and tier placement.
CrowdStrike (Onum) — Operational fabric for Falcon telemetry with strong transformation; routing and selective forwarding still maturing.
Securonix EON — Traditional connectors and indexing; no first-party streaming data fabric.
Google Chronicle — High-speed lake ingestion, but no inline filtering, routing, or streaming placement logic.
Vendor Comparison Overview
A high-level summary of how Fluency, Microsoft Sentinel, Securonix, CrowdStrike, Splunk, and Google Chronicle stack up against the NG-SIEM architectural requirements.
| Vendor | NG-SIEM Grade | Summary |
|---|---|---|
| Fluency SIEM | A | Streaming-first architecture with identity hierarchy, automatic case building, noise reduction, and native data lake routing. |
| Microsoft Sentinel | C | Identity progress is real, yet streaming analytics, case automation, and fabric capabilities lag. |
| Securonix EON | B– | Behavioral analytics exist, yet workflows and lake routing rely heavily on manual work and services. |
| CrowdStrike Falcon SIEM | B | Strong identity and behavioral story inside the Falcon ecosystem, tempered by limited fabric and cross-source streaming. |
| Splunk Enterprise Security | C– | Powerful platform with mature enrichment, but remains search-centric without streaming fabric or automatic casework. |
| Google Chronicle | C– | Lightning-fast lake with flexible schema, yet lacks identity-first workflows and streaming automation. |
Detailed NG-SIEM Comparison
Each criterion is shown with pass/partial/fail indicators. Partial means the capability exists only inside a proprietary ecosystem or requires heavy manual effort.
| NG-SIEM Criterion | Fluency | Microsoft Sentinel | Securonix EON | CrowdStrike SIEM | Splunk ES | Google Chronicle |
|---|---|---|---|---|---|---|
| Real-Time Streaming Analytics | Grade A — native streaming engine; no reliance on scheduled queries. | Grade D — KQL and scheduled analytics stand in for streaming. | Grade C — UEBA available, but processing is not streaming-native. | Grade C — strong internal telemetry yet depends on indexed searches. | Grade D — search-centric architecture with no streaming layer. | Grade D — fast ingestion, but analytics run via search, not streaming. |
| Behavioral and Stateful Detection | Grade A — inline seasonality, clustering, thresholds, and state models. | Grade C — rule-first detection with UEBA add-ons. | Grade B — strong UEBA foundation with behavioral models. | Grade B — rich behavior for Falcon telemetry; external data reverts to rules. | Grade C — UEBA exists, yet correlation rules dominate. | Grade C — detection remains largely rule-driven. |
| Identity-First Correlation | Grade A — native identity hierarchy links people, devices, applications, and services. | Grade B+ — IdentityInfo schema unifies Microsoft telemetry; cross-vendor links need build-out. | Grade B — IAM/IGA enrichment delivers attribution, yet full graphing needs services. | Grade A- — unified human and machine identity when Identity Protection is enabled. | Grade B– — identity enrichment via lookups requires manual maintenance. | Grade C+ — IAM mapping exists without native identity-first workflows. |
| Case-Based Investigation Workflow | Grade A — streaming cases assemble automatically with evolving evidence. | Grade B– — incident grouping exists, though narrative remains analyst-built. | Grade B — risk-based incidents create strong cases with analyst-directed workflow. | Grade B+ — Falcon incidents excel inside the ecosystem, limited for third-party feeds. | Grade C+ — Episode Review groups alerts but evidence assembly stays manual. | Grade D — investigation timelines only; no native case object. |
| Noise Reduction Through Event Hierarchy | Grade A — scoring, clustering, and deduplication collapse telemetry into signals. | Grade C — alert volume remains high; grouping requires tuning. | Grade B– — correlation reduces noise but still leans on rules. | Grade B– — strong reduction inside Falcon; mixed sources vary. | Grade D+ — alert load mirrors correlation searches without hierarchy. | Grade C — some grouping, yet no true event hierarchy. |
| Streaming Data Fabric | Grade A — full streaming fabric with native routing, filtering, enrichment, and Parquet placement. | Grade D — Event Hub ingestion lacks filtering, routing logic, and tier placement. | Grade D+ — traditional connector/index pipeline with no streaming fabric. | Grade B — operational fabric for Falcon telemetry with transformation, while routing and filtering continue to mature. | Grade B– — achievable only with Cribl or similar third-party fabric. | Grade C– — strong lake ingestion without streaming placement or filtering. |
Key Findings
Fluency is the only platform that satisfies all NG-SIEM criteria.
Streaming analytics, behavioral detection, identity hierarchy, case automation, noise reduction, and native data lake routing are delivered out-of-the-box.
Legacy SIEMs fail for predictable architectural reasons.
They rely on rule stacking, dashboard-driven correlation, manual case assembly, and expensive index-first storage.
Emerging SIEMs get close but still depend on manual workflows.
Securonix and Chronicle offer partial capabilities yet still require analysts to stitch identity, evidence, and response manually.
Conclusion
A Next-Generation SIEM is defined by streaming analytics, behavior-driven detection, identity-first correlation, case automation, noise reduction, and data lake routing. Fluency is built around these architectural pillars. Legacy SIEMs cannot inherit these capabilities without rebuilding their core.
NG-SIEM architecture is the foundation that makes AI useful. Without it, SOC teams remain trapped in alert queues and expensive storage bills.