Investigate a meaningful case
Review how UEBA, correlation, clustering, and risk scoring connect activity and prioritize the work. Inspect source evidence behind the conclusion.
Fluency vs. Microsoft Sentinel
Why modern SOCs are switching from Microsoft Sentinel. Not just faster—smarter, simpler, and made for the real world.
AI Philosophy
How Does Sentinel's AI Approach Differ from Fluency?
AI in cybersecurity isn't about automation for its own sake. It's about precision, decision-making, and trust. Microsoft treats AI as a co-pilot—summarizing, suggesting, reacting. Fluency treats AI as a front-line analyst, capable of assessing threats, taking action, and filtering what actually matters.
BFluency
CMicrosoft Sentinel
- Microsoft Sentinel
Microsoft treats AI as a co-pilot—summarizing, suggesting, reacting. It's a rule-based engine with reactive tooling, lacking persistent memory and integrated workflow execution.
- • Copilot serves as a summarizer and search assistant, not a decision-maker
- • No persistent memory or context-passing — AI outputs are stateless and disconnected
- • Relies on KQL and Logic Apps — separate tools requiring human glue
- • AI roadmap exists but lacks cohesive, integrated workflow execution
- • Fundamentally a rule-based engine with reactive tooling
- Fluency
Fluency is built to replace Tier 1 and Tier 2 analysts—not supplement them. The platform ingests, validates, triages, and acts on events in real time, focusing analysts on the rare, novel, and strategic.
- • Multi-Context Processing (MCP) enables streaming state awareness and memory across events
- • AI is structured into workflows: Validate → Scope → Respond → Review
- • Uses FPL—a JavaScript-based language for real-time AI action and analysis
- • Automatically responds to common behaviors, reducing analyst noise by orders of magnitude
- • Transforms SOC operations by making the system the first—and often only—responder
Detection Philosophy
Detection by Query vs. Detection by Process
Microsoft Sentinel approaches detection through a database-centric lens. Its design is rooted in searching static logs using KQL (Kusto Query Language). Fluency takes a fundamentally different approach—one centered on process execution and real-time state.
- Microsoft Sentinel
Microsoft Sentinel approaches detection through a database-centric lens. Its design is rooted in searching static logs using KQL (Kusto Query Language), treating detection as a saved search problem.
- • Delayed Insight: Queries must run on a schedule, introducing lag into every detection cycle
- • Match-Heavy Logic: KQL favors exact matching, not analytic reasoning—resulting in brittle detections
- • Artificial Complexity: KQL is a custom language bolted onto an outdated detection model
- • SOAR Reliance: Because detection is passive, SOAR tools are overburdened trying to compensate for missed context
- • No Stateful Logic: There's no concept of streaming behavior or process continuity—each event stands alone
- Fluency
Fluency doesn't store logs and hope someone queries them later. It builds workflows from events the moment they happen. Detection happens in motion—using real-time state, memory, and logic to drive response.
- • Real-Time: Data is processed as it arrives, with detections triggering sub-second responses
- • Executable Logic: Built on FPL—a JavaScript-compatible language with true functions, loops, and conditionals
- • Streaming State: Behavioral chains, thresholds, and anomalies are tracked live—not retrospectively guessed
- • Fewer Playbooks: Because the system detects and acts on context, fewer SOAR routines are needed to patch logic gaps
- • True Processes: Every detection path is a structured process—not a report waiting for a query
Direct Comparison
Head-to-Head: Fluency vs Microsoft Sentinel
See how Fluency's real-time AI-driven approach compares to Microsoft Sentinel's traditional query-based detection. The numbers don't lie—modern security requires modern solutions.
| Feature | Fluency | Microsoft Sentinel |
|---|---|---|
| Detection Engine | MCP with memory & AI | KQL-based rules |
| Latency | Sub-second | Minutes (via Log Analytics) |
| Vendor Lock-in | None | Tied to Azure ecosystem |
| Ease of Use | Simple JS-like language | KQL and custom schemas |
| Automation | Built-in SOAR-lite, API-ready | Logic Apps / Power Automate |
- Real-time processing.
- Fluency processes events as they arrive with sub-second latency, while Sentinel relies on scheduled queries with minutes of delay.
- Vendor independence.
- Fluency works with any data source and cloud provider, while Sentinel locks you into the Azure ecosystem.
- Modern language.
- Fluency uses JavaScript-compatible FPL for easy development, while Sentinel requires learning KQL and custom schemas.
- Built-in automation.
- Fluency includes SOAR-lite capabilities out of the box, while Sentinel requires separate Logic Apps integration.
- AI-driven workflows.
- Fluency structures AI into executable workflows, while Sentinel treats AI as a passive assistant.
- Memory and context.
- Fluency maintains state across events for behavioral analysis, while Sentinel processes each event in isolation.
Don't settle for near real-time. Go Fluency-fast.
Contact SalesWhat to evaluate
Ask for proof across the product and the delivery.
Use the same operational questions when evaluating every SIEM. Fluency brings a strong detection foundation, broad analysis, and hands-on support to that evaluation.
Answer a management question
Compare coverage or health across periods, analyze usage and billing, and review a capacity forecast with its assumptions. Assess certification evidence and gaps.
Establish the deployment path
Review source access, data verification, tenant boundaries, and ongoing support. Test a guided Claude or Codex workflow and inspect the result in Companion.