International business machines corporation (20240104207). SECURITY THREAT DETECTION USING COLLABORATIVE INTELLIGENCE simplified abstract

SECURITY THREAT DETECTION USING COLLABORATIVE INTELLIGENCE

Organization Name

international business machines corporation

Inventor(s)

June-Ray Lin of Taipei City (TW)

Narayana Aditya Madineni of Bundall (AU)

SECURITY THREAT DETECTION USING COLLABORATIVE INTELLIGENCE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240104207 titled 'SECURITY THREAT DETECTION USING COLLABORATIVE INTELLIGENCE

Simplified Explanation

The patent application relates to generating a security threat prediction using a system that analyzes public and proprietary data graph models.

• The system includes a memory storing computer executable components and a processor executing these components.
• A prediction component analyzes public data graph models to generate a primary security threat determination and a secondary security threat prediction based on proprietary data graph models.
• The proprietary data graph model contains scrubbed proprietary security threat data from a source, with an obtaining component obtaining an agreement to share this data with another source.

Potential Applications

This technology can be applied in cybersecurity, threat intelligence, risk management, and predictive analytics industries.

Problems Solved

This technology helps in predicting security threats by analyzing both public and proprietary data graph models, providing a more comprehensive threat assessment.

Benefits

The benefits of this technology include improved security threat predictions, enhanced risk mitigation strategies, and better decision-making based on advanced analytics.

Potential Commercial Applications

The potential commercial applications of this technology include cybersecurity software, threat intelligence platforms, risk assessment tools, and predictive analytics solutions.

Possible Prior Art

One possible prior art could be the use of machine learning algorithms to analyze data for security threat predictions. Another could be the use of data graphs in cybersecurity for threat analysis.

What are the limitations of this technology in real-world applications?

The limitations of this technology in real-world applications may include the need for continuous updates of data models, potential biases in data sources, and the challenge of integrating with existing security systems.

How does this technology compare to existing security threat prediction methods?

This technology stands out by combining public and proprietary data graph models for security threat predictions, providing a more comprehensive and accurate assessment compared to traditional methods that rely solely on public data sources.

Original Abstract Submitted

one or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to generating a security threat prediction. a system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. the computer executable components can comprise a prediction component that analyzes a public data graph model to generate a primary security threat determination, wherein the prediction component can further generate a secondary security threat prediction based on the primary security threat determination and on a proprietary data graph model, wherein the proprietary data graph model comprises proprietary security threat data from a source, and wherein the proprietary security threat data has been scrubbed of source-identifiers. an obtaining component can obtain an agreement from the source to share the proprietary security threat data with another source that has access to the proprietary data graph model.