Cybersecurity System for Real-Time Entity Profiling and Risk Mitigation
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Solution Overview
Problem
Current cybersecurity systems lack an efficient mechanism to identify, analyze, and manage information security threats such as spam, phishing, and network risks in real-time, leading to potential data breaches and productivity losses.
Innovation Solution
A method and system that collect data from various sources, execute algorithms to generate consistent labels, detect entities, create profiles for each entity by capturing behavior over time, and predict risks to proactively update security policies and recommend mitigations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time threat identification and analysis is implemented, then security response effectiveness is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system segments cybersecurity threat analysis into distinct functional modules: data collection from multiple sources, entity detection and profiling, behavior analysis, risk generation, and mitigation recommendation. Each module processes specific aspects of security monitoring independently, reducing overall system complexity while maintaining comprehensive real-time analysis capabilities.
Solution Approach 2:
The system performs preliminary actions by pre-establishing entity profiles, behavior baselines, and risk models before actual threats occur. This allows the system to quickly match and analyze incoming security events against pre-computed data structures, improving real-time response effectiveness without proportionally increasing processing complexity during incident response.
2Measurement precision
If comprehensive data collection from multiple sources is performed, then threat detection accuracy is improved, but data processing time and computational load increase
Solution Approach 1:
The system extracts and isolates critical security-relevant features from comprehensive multi-source data, separating essential threat indicators from redundant information. By focusing computational resources on extracted key features rather than processing all raw data equally, the system maintains high threat detection accuracy while reducing overall data processing time and computational load.
Solution Approach 2:
The system applies different processing qualities and depths to different data sources and entity types based on their security relevance. High-priority data sources and critical entities receive more intensive analysis, while less critical data undergoes lighter processing, optimizing the balance between detection accuracy and processing efficiency across the heterogeneous data landscape.
3Measurement precision
If entity profiling and behavior analysis are performed, then false alarm reduction is improved, but computational resources and processing time increase
Solution Approach 1:
The system applies partial profiling and behavior analysis selectively to entities based on their risk level and activity patterns. Rather than uniformly profiling all entities, the system focuses computational resources on partially profiling high-risk entities that exhibit suspicious behavior, reducing overall computational resource consumption while maintaining effective false alarm reduction for critical security events.
4Reliability
If proactive risk management and predictive mitigations are implemented, then security posture is improved, but system complexity and operational overhead increase
Solution Approach 1:
The system implements self-service capabilities by automatically generating risk assessments, predicting potential threats, and recommending mitigations without requiring manual security analyst intervention for each event. The system serves itself by autonomously processing security data, maintaining entity profiles, and producing actionable insights, thereby improving security posture while minimizing operational overhead and manual workload.
Data Source
AI summary
Disclosed is a method and a system for using techniques to stitch cybersecurity, generate network risks and predictive mitigations. The method includes collecting data from several data sources and labeling events. The method includes creating a profile for each entity observed in the data with the behavior of the profile determined through the analytical analysis of the events in which the entity participates including the transference of labels from events to the entity. One or more profiles of an organization are identified that have changed and the change is processed using specific attack sequence detection to identify one or more risks associated with each profile. The method further includes notifying one or more users associated with the one or more profiles based on the one or more risks.