Alpha Level provides a behavioral threat‑detection overlay for security operation centers, automatically filtering out benign alerts and surfacing genuine attacks. Its self‑learning engine uses anomaly detection and team feedback to continuously improve precision, while a privacy‑first design keeps all data on‑premises with no external storage. The solution delivers instant, explainable alert decisions, helping security teams reduce noise and focus on real threats.
Funding
Funding not disclosed
Founders
Product
Problem
Security operation centers (SOCs) are overwhelmed by high volumes of alerts, many of which are false positives, leading to alert fatigue and delayed response to genuine threats.
Solution
Alpha Level offers a behavioral threat detection overlay that automatically analyzes incoming alerts, applies anomaly detection, and provides instant, confidence‑rated decisions with clear explanations. The system reduces alert noise by filtering out benign events and highlights real attacks, allowing analysts to focus on high‑priority incidents. A self‑learning feedback loop incorporates analyst actions to continuously improve detection accuracy. The platform is built with a privacy‑first architecture, keeping all data on‑premise and eliminating external storage or third‑party access, thereby maintaining compliance and data security.
Target Audience
Primary customers are SOC teams and security analysts in enterprises that require high‑volume alert management and on‑premise data protection.
Features
- Real‑time anomaly detection that classifies alerts with confidence scores and explanatory context
- Autonomous noise reduction that operates without continuous human monitoring
- Self‑learning feedback mechanism that refines models based on analyst responses
- On‑premise deployment ensuring all data remains within the customer’s infrastructure
- Dynamic alert monitoring that enables removal of exclusions in existing detection logic
- Deterministic, trustworthy alert classification to support rapid incident response