Browser security platform preventing AI, SaaS, and data leakage risks
LayerX delivers endpoint security as a browser extension, protecting against AI and SaaS data leakage, malicious extensions, and zero-hour web attacks. The tech stack reveals a sophisticated ML-ops infrastructure: Python + TensorFlow + PyTorch running on SageMaker with automated retraining pipelines (DVC, MLflow), Kafka for real-time event ingestion, and Kubernetes deployment — indicating security threat detection is model-driven and continuously improving. Senior-heavy hiring (6 of 7 roles) focused on engineering and data signals a team scaling detection depth and production reliability rather than breadth.
LayerX is a New York-based security vendor founded in 2021 that ships as an enterprise browser extension. The platform monitors user interactions across browsers, SaaS applications, and AI tools to detect and prevent data leakage, identity abuse, and browser-based attacks without degrading user experience. Core capabilities include AI and SaaS discovery, data loss prevention, malicious extension blocking, and identity governance across work and personal identities. The product handles scale challenges across millions of browser endpoints and processes large event datasets in real-time to surface actionable threat signals.
AWS, GCP, Azure, Node.js, TypeScript, Python, Kafka, PostgreSQL, Kubernetes, Temporal, TensorFlow, PyTorch, SageMaker, DVC, MLflow, and Salesforce. Also supports Chrome, Edge, Firefox, and Safari.
Real-time ML-driven threat detection for browser security, including automated model retraining pipelines, security threat behavior analysis, data models for governance, and continuous monitoring of user activity across web and SaaS channels.
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