Full Report
The blog post discusses the evolution of Huntress' data analysis in response to scaling challenges and how we transitioned to a custom detection engine.
Analysis Summary
# Industry News: Huntress Re-Engineers Data Pipeline to Combat Scaling Bottlenecks
## Summary
Huntress has officially transitioned from a third-party integrated detection model to a custom-built detection engine leveraging AWS-hosted OpenSearch. This strategic shift addresses the performance limitations of their previous Elastic-based "Security app" architecture, which struggled to process the 30TB of daily data generated by Huntress’ two million+ monitored endpoints.
## Key Details
- **Date:** August 31, 2023
- **Companies Involved:** Huntress, Amazon Web Services (AWS)
- **Category:** Product Update / Infrastructure Re-engineering
## The Story
As Huntress scaled to manage over two million agents, their legacy data analysis pipeline—which relied on indexing documents in Elasticsearch before running detection rules via the Kibana Security app—reached a breaking point. The "backward-looking" nature of this system created detection latencies ranging from one minute to several hours and imposed a strict cap of 100 signals per rule execution.
To solve this, Huntress developed a custom detection engine. This new architecture moves detection "upstream," allowing for real-time analysis as data flows through the pipeline rather than waiting for it to be indexed. By leveraging AWS OpenSearch and a parallelized processing job, Huntress can now handle their massive data load (approximately 900TB per month) more efficiently, reducing both cost and time-to-detection.
## Business Impact
### For the Companies Involved
- **Huntress:** Achieves significant operational cost savings by reducing reliance on expensive third-party licensing and heavy indexing. It also allows their SOC to respond to threats faster, strengthening their core value proposition.
- **AWS:** Solidifies its role as the backbone for high-growth cybersecurity SaaS providers through the adoption of OpenSearch.
### For Competitors
- **Competitive Pressure:** This move sets a high bar for Managed Detection and Response (MDR) providers. Competitors still relying on "out-of-the-box" SIEM or ELK stack configurations may struggle to match Huntress' speed and cost-efficiency at scale.
### For Customers
- **Reduced MTTR:** End users benefit from a significant reduction in Mean Time to Respond (MTTR), as the engine no longer suffers from the "batch processing" delays of the previous system.
- **Pricing Stability:** Improved backend efficiency helps Huntress maintain competitive pricing despite exponential data growth.
### For the Market
- **Infrastructure Maturity:** This reflects a broader trend where cybersecurity firms are maturing beyond standard tools to build proprietary, specialized infrastructure to handle "Big Data" security challenges.
## Technical Implications
The primary innovation is the transition to a **stream-processing detection model**. By utilizing the Sigma rule format and converting it into a custom-engine-compatible format, Huntress decoupled detection from database indexing. This eliminates the "100 match limit" and allows for immediate signal generation upon data ingestion.
## Strategic Analysis
- **Market Positioning:** Huntress is positioning itself as a "technology-first" MDR that can handle enterprise-scale data volumes while maintaining the agility required for the SMB/MSP market.
- **Competitive Advantage:** Owning the detection engine allows Huntress to innovate faster without waiting for third-party platform updates (e.g., Elastic or Kibana feature releases).
- **Challenges:** Maintaining a custom-built engine requires significant ongoing engineering overhead compared to off-the-shelf solutions.
## Industry Reactions
- **Analyst Perspective:** Market analysts view this as a necessary rite of passage for "Unicorn" status security firms; to achieve true scale, proprietary data handling is essential.
- **Market Response:** Positive reception from the MSP community, who prioritize rapid detection in the face of ransomware and living-off-the-land attacks.
## Future Outlook
- **Predictive Detections:** With a more flexible engine, expect Huntress to incorporate more complex behavioral and ML-based detections that were previously too "expensive" to run on the old architecture.
- **Managed SIEM Expansion:** This infrastructure likely serves as the foundation for their expanding Managed SIEM offerings.
## For Security Professionals
Practitioners should note that as data volumes increase, the "latency of ingestion" becomes a critical security gap. When evaluating MDR or EDR vendors, ask specifically whether detections occur **in-stream** or **post-indexing**, as the latter can introduce dangerous delays in high-velocity attack scenarios.