Entity-Oriented Data Fabric for Cyber Threat Relationship Analysis
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Solution Overview
Problem
Small and medium businesses lack effective cybersecurity solutions to counter sophisticated cyber threats due to high costs and complexity, with existing firewalls and antivirus systems being inadequate against nation-state adversaries.
Innovation Solution
Creating an entity-oriented data fabric (EODF) within a protected environment to identify and manage entities, extract contextual features, and establish relationships, providing a unified view of entities and threats, enhancing machine-based analytics for advanced threat detection and automated responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If off-the-shelf firewalls and anti-virus systems are deployed, then basic security coverage is achieved, but they are inadequate against sophisticated nation-state attacks
Solution Approach 1:
The patent combines multiple security functions (firewall, antivirus, threat intelligence, entity analytics) into a unified security platform that creates an entity-oriented data fabric. This integration allows small businesses to access enterprise-grade protection capabilities without managing multiple separate complex systems, directly addressing the contradiction between security effectiveness and system complexity.
Solution Approach 2:
The security platform provides multi-functional capabilities including entity identification, relationship mapping, threat detection, and automated response within a single system. This universal approach enables comprehensive security coverage against various attack types (ransomware, business email compromise, financial fraud) without requiring separate specialized systems for each threat type.
2Reliability
If enterprise-grade cyberthreat protection is implemented, then advanced threat detection capability is achieved, but cost and complexity become prohibitive for small businesses
Solution Approach 1:
The patent creates a virtualized entity-oriented data fabric that replicates enterprise-grade security analytics capabilities in a cloud-based platform accessible to small businesses. Instead of requiring businesses to build and maintain expensive enterprise security infrastructure, they can access equivalent protection capabilities through the cloud platform at a fraction of the cost.
Solution Approach 2:
The system implements automated entity identification, relationship mapping, and threat detection without requiring extensive manual configuration or specialized security personnel. The platform automatically ingests data from multiple sources, builds entity graphs, and performs analytics, enabling small businesses to access advanced protection without the operational overhead that drives up costs.
3Measurement precision
If comprehensive entity data collection and relationship mapping is performed, then threat detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent replaces manual data processing and analysis with automated machine learning algorithms and entity-oriented analytics. The system automatically ingests data from multiple sources, identifies entities, maps relationships, and detects threats using computational algorithms, eliminating the need for manual analysis while maintaining high accuracy in threat detection.
Solution Approach 2:
The entity-oriented data fabric serves as an intermediary layer that structures and organizes complex data from multiple sources into a unified entity graph model. This intermediate representation simplifies the complexity of raw data by organizing it into meaningful entities and relationships, making it easier to process and analyze while improving detection accuracy.
Data Source
AI summary
Systems, methods, and storage media for creating an entity-oriented data fabric (EODF) for a protected environment (PE) are described, the system configured to identify a plurality of entities associated with the PE; obtain first data associated with at least one entity from the PE; identify a relationship with at least one other entity for each of the entities; create a plurality of objects, where each of the plurality of objects is associated with one or more entities and a portion of the first data; store first information related to the entities, first data, relationships, and the objects; create the EODF for the PE, the EODF comprises a data framework for providing a unified view of the plurality of entities and relationships, the EODF facilitates discovery of compromised entities or entities vulnerable to being compromised; and providing a visualization for displaying the first information on a user interface.