Topic: Glasswing impact on Q2 pipeline & core vs. non-core ARR breakdown
Key points:
Glasswing has two customer cohorts: those who have seen it (seeking insights on exploitable vulnerabilities and noise reduction) and those outside (assessing if they must halt projects for remediation).
Core platform: detection & response (55% of total ARR) grew 7% YoY (net of churn); combined with exposure management, core grew 2% in Q1.
Sales organization productivity increased in Q1 under new leader Alan, with good execution on operational details.
Mgmt stance: Neutral — early days for Glasswing impact; core growth modest but execution improving.
Q7 — Analyst
Topic: Monetization of AI-driven remediation & go-to-market changes
Key points:
No incremental charge for Glasswing; monetization expected via VM-to-Exposure Command upgrade acceleration.
MDR expansion: active response with automation/AI is a potential monetization area, but too early; resources being recalibrated toward machine-speed active response.
Go-to-market: Alan tightened focus on selling core (DNR, Exposure, Command platform); tighter pipeline builds and consistent target hitting.
Mgmt stance: Bullish on core platform focus and execution trends; cautious on MDR monetization timing.
Kenzo is an AI alert processing engine for high-quality investigations at scale; integration is in progress, not done.
Rollout to customers starts in the next couple of months and continues through the rest of 2026.
Kenzo excels at deduplication, investigation, and remediation at machine speed; Rapid7 is extending the model to wider data sources and adding response options based on environment knowledge.
Mgmt stance: Bullish on Kenzo’s technology and integration potential; cautious on timeline (still in development).
Q9 — Adam Borg
Topic: Moats against frontier models moving from vulnerability identification to remediation
Key points:
Three moats: (1) Frontier models are cost-inefficient for exploitability analysis vs. specialized VM systems; (2) Exploitability requires understanding environment configuration, controls, and intersections—specialized data Rapid7 has; (3) Autonomous response needs trust and knowledge base—frontier models are not trusted for making configuration changes due to risk of catastrophic errors.
Rapid7 leverages frontier models internally but focuses on cost-effective, trusted active response.
Mgmt stance: Bullish on moats — emphasizes cost, specialized knowledge, and trust as barriers; neutral on frontier model threat.