TenantTwin is a multi-tenant triage optimizer for MSSPs who operate dozens or hundreds of small customers on shared SIEM/XDR stacks and complain (on r/cybersecurity and r/MSSP) that multi-tenant noise and per-tenant tuning are unmanageable. Existing tools barely distinguish between a Fortune 500 and a 50-seat law firm, leading to either blanket suppression or endless tuning. TenantTwin builds an AI profile for each tenant (size, tech stack, user behavior, business hours, critical apps) and uses it to re-label and re-prioritize alerts tenant-by-tenant.

Targets: MSSPs and MDR providers using Microsoft Sentinel, Sumo Logic, LogRhythm, or Splunk MSP deployments for 20–500 SMB tenants.
Value: Learns per-tenant normal behavior and auto-suggests rule thresholds and exceptions, drastically reducing the time senior analysts spend on per-customer tuning and Tier-1s spend on obvious false positives for sleepy tenants.
Differentiation: Rather than being yet another SOC copilot, it focuses strictly on multi-tenancy pain – including automated onboarding baselines for new tenants, cross-tenant anomaly detection ("this customer suddenly looks like your other breached customer"), and per-tenant health scores. This directly maps to repeated MSSP Reddit threads and G2 reviews of SIEMs complaining that “multi-tenant management is bolted-on and not intelligent at all.”