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Fluency vs. Microsoft Sentinel

Why modern SOCs are switching from Microsoft Sentinel. Not just faster—smarter, simpler, and made for the real world.

🤖 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. It's not just a difference in implementation—it's a difference in worldview.

BFluency

CMicrosoft Sentinel

Microsoft Sentinel’s AI Limitations

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’s AI Design and Philosophy

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. Your analysts don’t waste time combing through routine alerts—they focus 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

This isn’t an enhancement of legacy SIEM—it’s a reinvention. Fluency’s AI is the analyst. Your team becomes the oversight, the judgment, the strategy.

💽 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’s Query-Driven Design

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’s Process-Centric Architecture

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.

At Fluency, detection is a process—an unfolding narrative of behavior—not just a timestamped event in a database. This change reduces response time, raises fidelity, and makes security operational again.

📊 Head-to-Head Comparison

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

Don’t settle for near real-time. Go Fluency-fast.

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