End-to-end ML model lifecycle for security AI — from experiment tracking and feature engineering to model serving, drift detection, and automated retraining. Purpose-built for SOC teams.
Monitor your entire ML pipeline from a single pane of glass — model performance, training jobs, drift alerts, and prediction volume.
Every tool your team needs to build, deploy, and maintain ML models that power autonomous threat detection.
Full experiment lifecycle management with metric logging, hyperparameter tracking, and run comparison. Track every training run with reproducible configurations and artifact versioning.
Centralized feature management with online (Redis) and offline (PostgreSQL) storage. Security-specific feature categories — network, endpoint, user behavior, threat intel, and entity.
Low-latency inference engine supporting ONNX, scikit-learn, LightGBM, and XGBoost. Single and batch prediction with endpoint health monitoring, error tracking, and latency metrics.
Statistical monitoring for data and concept drift using KS tests, PSI, Jensen-Shannon divergence, and Wasserstein distance. Automatic severity classification and alert generation.
Pipeline-driven model retraining triggered by drift, schedule, analyst feedback, or new data. Includes data validation, feature engineering, training, evaluation, and champion-challenger comparison.
Controlled experiments comparing model versions in production. Traffic splitting with consistent hashing, statistical significance testing, and automatic winner determination.
From experiment to production — a six-stage pipeline with quality gates, champion-challenger evaluation, and automated drift-triggered retraining.
A modular architecture covering experiment tracking, data management, model governance, and production operations.
Real-time threat detection powered by ML. Models trained on labeled attack data with MITRE technique mapping for precise alert classification.
UEBA models that learn normal user and entity behavior, then flag anomalies. Continuous learning adapts to organizational patterns over time.
Unsupervised models that identify unknown threats and zero-day attacks. Statistical and deep-learning approaches across network, endpoint, and cloud telemetry.
Risk scoring and threat prediction models that forecast potential incidents before they materialize. Capacity planning and resource optimization for SOC operations.
One-click deployment with canary releases, quality gates (95% accuracy, <1% FPR), and automated rollback.
Primary target — real-time threat detection on incoming security events.
User and entity behavior analytics for insider threat detection.
Enrichment models for IOC scoring and threat classification.
Risk scoring models that trigger automated containment playbooks.
Native support for leading ML frameworks and security standards. Train in any framework, serve with ONNX for optimal latency.
Deploy ML models to your detection engine with confidence. Automated drift monitoring, retraining, and canary deployments — production-grade from day one.