AI-Driven Cybersecurity Management for Real-Time Risk Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprises face challenges in managing cybersecurity risks across complex computing systems due to the volume and diversity of data generated, which often leads to inefficient prioritization and remediation of security vulnerabilities, especially in environments with multiple devices and infrastructure types.
Innovation Solution
A cybersecurity management system integrating artificial intelligence (AI), machine learning (ML), and extended reality (XR) to ingest, analyze, and visualize large datasets in real-time, enabling hyperautomation, digital assistants, and immersive XR interfaces for enhanced risk management and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional cybersecurity management methods are used to handle large volumes of security data, then data processing capacity is limited, but the time to identify and prioritize security risks increases significantly
Solution Approach 1:
The patent replaces traditional manual cybersecurity analysis methods with artificial intelligence and machine learning systems. The AI/ML models automatically ingest, analyze, and prioritize security data from multiple sources, substituting human analysts' mechanical processing with intelligent automated systems that can handle large volumes of data simultaneously, thereby increasing processing capacity while reducing the time to identify and prioritize risks.
Solution Approach 2:
The patent introduces an intermediary layer of AI/ML models between raw security data and decision-makers. This intermediary automatically processes, analyzes, and prioritizes security events, transforming raw data into actionable insights. The intermediary handles the complexity of data processing, allowing faster risk identification and prioritization without overwhelming human analysts.
2Reliability
If comprehensive security monitoring is implemented across all devices and infrastructure, then security coverage is improved, but system complexity and data management burden increase
Solution Approach 1:
The patent implements a universal cybersecurity management platform that can monitor and analyze multiple types of devices and infrastructure through a single system. The AI/ML models are designed to handle diverse data sources including endpoints, networks, cloud services, and IoT devices, providing comprehensive security coverage while managing complexity through unified multi-functional architecture rather than separate specialized systems.
Solution Approach 2:
The patent segments the complex security monitoring system into modular components: data ingestion modules, AI/ML analysis modules, visualization modules, and response modules. Each component handles specific tasks independently, allowing comprehensive monitoring across all devices while managing complexity through modular design. The segmented architecture enables independent scaling and maintenance of different monitoring functions.
3Ease of operation
If manual prioritization and remediation processes are used for security vulnerabilities, then resource allocation decisions are made, but the speed of remediation and operational efficiency decrease
Solution Approach 1:
The patent implements self-service capabilities where the AI/ML system automatically prioritizes security risks and generates remediation recommendations without requiring manual intervention. The system autonomously analyzes security data, assesses risk levels, and provides prioritized action items, enabling rapid remediation while maintaining efficient resource allocation. This self-service approach eliminates bottlenecks in manual decision-making processes.
Solution Approach 2:
The patent incorporates feedback loops where the AI/ML system continuously monitors remediation progress and adjusts prioritization based on emerging threats and changing risk landscapes. The system provides real-time feedback on security posture and remediation effectiveness, enabling dynamic resource allocation that balances ease of operation with accelerated remediation speeds through continuous optimization.
Data Source
AI summary
A method includes ingesting cybersecurity data collected from a set of data sources of a computing system of an enterprise to obtain input data, wherein each data source of the set of data sources generates data related to cybersecurity within the computing system, processing the input data to generate an analysis output comprising an assessment of cybersecurity risk within the computing system, and performing, based on the analysis output, at least one action to manage cybersecurity for the computing system. Performing the at least one action includes at least one of using a digital assistant for managing the cybersecurity of the computing system, or using an extended reality system to access a virtual environment for managing the cybersecurity of the computing system.