Scaling a Real-Time Threat Detection Platform 10x

Envadel provided 4 senior engineers under the Staff Augmentation model. Each engineer was embedded directly into one of the client's existing squads, participating in their standups, using their tooling (GitLab, Linear, Slack), and following their code review processes. The augmented engineers focused on the platform scalability initiative while the client's engineers continued feature development.

The technical approach involved decomposing the monolithic detection engine into event-driven Go microservices, replacing the single-threaded event processor with a Kafka-based streaming pipeline, and implementing Kubernetes Horizontal Pod Autoscaler (HPA) for elastic scaling. The migration was executed incrementally, with traffic gradually shifted from the old engine to the new pipeline using feature flags.

A parallel workstream focused on the ML-based threat scoring pipeline. The existing Python-based ML models were re-deployed as optimized inference services with batch prediction capabilities, reducing the compute cost per prediction by 75% while maintaining model accuracy. A new Grafana/Prometheus observability stack was deployed to provide real-time visibility into detection latency, throughput, and false positive rates.

The core detection engine was rewritten in Go, chosen for its superior concurrency model (goroutines), low memory footprint, and predictable garbage collection behavior — critical requirements for sub-second processing guarantees. Each detection rule category (network anomaly, endpoint behavior, authentication events, data exfiltration) was implemented as an independent microservice with its own Kafka consumer group.

Apache Kafka served as the central event bus with partitioned topics per event source type. The pipeline processed events through three stages: ingestion (normalization and enrichment), correlation (rule matching and threat scoring), and action (alert generation, automated response). Each stage scaled independently via Kubernetes HPA based on consumer lag metrics, enabling the system to absorb traffic spikes of 5x baseline without latency degradation.

Elasticsearch was deployed as the hot-warm-cold storage tier for event retention and forensic investigation. A custom indexing strategy with time-based rollover and force-merge optimization reduced storage costs by 40% while maintaining sub-second search latency for the most recent 30 days of events. Redis was used for real-time state management: tracking active sessions, IP reputation scores, and threat indicator caches with TTL-based expiration.

The ML pipeline was refactored to run on a separate Kubernetes namespace with GPU node pools for model training (Python/TensorFlow) and CPU-optimized pods for inference (Go-wrapped TensorFlow Lite). Model updates were deployed via a canary mechanism: new models received 10% of traffic alongside the production model, with automated comparison of false positive rates before promotion.

As staff augmentation, Envadel engineers followed the client's existing delivery processes: 1-week sprints, trunk-based development with feature flags, pair programming for complex changes, and a strict "no direct commits to main" policy. Code reviews required approval from at least one client engineer and one Envadel engineer for cross-pollination.

Envadel's delivery manager conducted bi-weekly check-ins with the VP of Engineering and a monthly executive review covering individual performance metrics, knowledge transfer progress, and scalability initiative milestones. A dedicated Slack channel provided real-time visibility between Envadel leadership and the client's engineering management.

Knowledge transfer was built into the engagement from the start. Each Envadel engineer conducted weekly "tech talks" (30-min internal sessions) covering the architectural decisions and patterns being introduced. By month 6, the client's internal engineers were independently building and deploying new detection microservices using the patterns established by the augmented team.