Arcanna

    Case Study

    Global MXDR Achieves Predictable Triage at Scale - 3× Faster P1/P2 Handling

    A global MXDR provider delivering 24/7 SOC coverage needed to scale across 100+ customer environments - without sacrificing consistency, SLA performance, or analyst oversight.

    Key Metrics & Benefits

    85%

    FASTER P1/P2 TRIAGE

    Reduced mean triage time from 20 minutes to 3 minutes.

    40%

    INCREASE IN PROCESSING CAPACITY

    Expanded throughput without adding headcount.

    100+

    CUSTOMER ENVIRONMENTS

    Stable triage decisions across tenants, shifts, and alert spikes.

    Scaling the MXDR Model Exposed Structural Gaps

    As customer environments expanded, the SOC's operating model began to show strain - across speed, consistency, and governance.

    100+ Customer Environments. Rising Alert Variability.

    Alert volumes were growing across tenants, tooling stacks, and threat patterns — increasing operational entropy across the SOC.

    P1/P2 Handling Was Too Slow and Unpredictable.

    Manual triage variability made it difficult to consistently protect SLA margins during peak alert periods.

    Expert Judgment Wasn’t Systematically Captured.

    Critical decision logic lived inside senior analysts rather than being reusable, validated, or reinforced across teams.

    Decision Quality Drifted Across Shifts.

    Without structured validation and feedback loops, the same alert could produce different outcomes depending on the analyst.

    Arcanna's Decision Layer Deployment

    Arcanna introduced a Decision Layer on top of the provider’s existing MXDR workflows - enabling safe, predictable triage at scale across 100+ customer environments.

    To operationalize analyst expertise, the provider deployed:

    • 19 MITRE-aligned triage models
    • Automated routing to the appropriate decision model
    • Continuous learning from analyst feedback
    • Guardrails: confidence thresholds, drift checks, full rollback
    • Human-in-the-Loop (HITL) oversight
    • Environment-aware modeling across tenants

    Arcanna learns directly from historical analyst decisions, predicting with high reliability how senior analysts would handle similar alerts across diverse environments and contexts.

    The result: predictable, explainable decisions across shifts, teams, and customer environments.

    Arcanna.ai decision control flow diagram

    From Triage Variability to Predictable Outcomes

    See how Arcanna's Decision Layer delivers 85% faster P1/P2 triage, a 40% increase in processing capacity, and consistent decisions across 100+ customer environments - with guardrails and Human-in-the-Loop oversight.