Key Capabilities / Features

AI-Powered Threat Identification: The SKL leverages advanced AI and vector embeddings to detect anomalies, unusual activities, and outliers across vast datasets, ensuring accurate and timely threat detection at scale.

Proprietary Ontology for Contextual Detection: Events are mapped to a three-layered ontology, enriching raw data with hierarchical context. This allows the system to categorize events as Normal, Abnormal, and Detection, providing clear, actionable insights for threat detection.

Dynamic Anomaly Detection: The SKL employs vector analysis and machine learning to identify patterns and deviations from normal behavior, enabling detection of unknown threats without relying on pre-configured rules or signatures.

Real-Time Event Correlation: Automatically groups related events across data sources to uncover larger attack patterns or ongoing campaigns, giving SOC teams a comprehensive view of emerging threats.

Hybrid Vector Space for Enhanced Detection: Combines dense and sparse vector embeddings to identify threats based on both semantic meaning and specific terms, ensuring precision even for novel or ambiguous data inputs.

Dynamic Models: Continuously adapts to changing threat detections and data drift without manual tuning, ensuring up-to-date and effective threat visibility for evolving attack methods.

Explainable AI for Threat Validation: All prioritized threats enable analysts to validate findings with confidence and act decisively.

High-Fidelity Detection at Scale: Processes petabytes of data, ensuring that no critical threat indicators are missed while maintaining efficiency and speed.

Cost-Effective Threat Detection: Optimizes data processing and storage costs by filtering and prioritizing critical security events, making threat detection accessible and sustainable for organizations of any size.

How we are different

Context-Enriched Threat Detection: Auguria’s Security Knowledge Layer™ uses a proprietary ontology and AI-driven vector embeddings to enrich every event with context, providing SOC teams with a clear understanding of why an event is normal, anomalous, or a high-confidence threat. This goes beyond basic detection, enabling faster, more accurate responses.

Real-Time Correlation and Precision: The SKL combines dense and sparse vector embeddings to detect threats based on both semantic meaning and specific terms, ensuring precision in identifying both known and unknown threats. Its ability to correlate related events in real-time uncovers hidden attack patterns, giving organizations a critical edge in proactive threat detection.

Self-Learning and Adaptability: Unlike static, rule-based systems, The SKL employs advanced AI models that continuously adapt to evolving alerts without requiring manual rule updates or playbook maintenance. This ensures its threat detection capabilities remain effective and future-proof.