Incident Management System for Cybersecurity Detection and Response
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Solution Overview
Problem
Current cybersecurity systems face challenges in timely and effective response to security incidents due to overwhelming alerts, shortage of skilled personnel, and lack of organized incident response methodologies, leading to prolonged recovery times and operational, legal, and regulatory consequences.
Innovation Solution
The development of an incident management and response system (IMRS) that utilizes enterprise service bus integration, workflow automation, digital cybertagging, ontology-based context models, human-computer collaborative learning, and machine learning to rapidly detect and respond to cybersecurity incidents, providing real-time situational awareness and adaptive incident response plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic security monitoring and malware detection are implemented, then security event detection capability is improved, but alert volume increases overwhelming the system and response effectiveness deteriorates
Solution Approach 1:
The patent extracts and separates true security incidents from false positive alerts by implementing a prioritization framework that identifies and focuses only on high-risk events. This filters out the overwhelming majority of low-risk alerts, allowing security personnel to concentrate on actual threats rather than being distracted by noise from basic monitoring tools.
Solution Approach 2:
The patent introduces an intermediary prioritization layer between detection and response actions. This intermediary framework assesses risk levels, contextualizes alerts, and determines response priorities, serving as a mediator that transforms raw alert volume into manageable, prioritized incident queues that improve response effectiveness.
2Productivity
If more security personnel are hired to handle increasing alerts, then incident response capability is improved, but operational costs increase
Solution Approach 1:
The patent implements automated self-service capabilities through machine learning models that automatically prioritize incidents, assess risk levels, and guide response actions. This automation reduces the manual labor required from security personnel, allowing the same team size to handle increased alert volumes without proportionally increasing operational costs.
Solution Approach 2:
The patent changes the parameter of incident prioritization from manual assessment to automated risk scoring. By transforming subjective human judgment into objective, algorithm-driven prioritization, the system increases response capability without requiring additional personnel, thereby avoiding increased operational costs.
3Adaptability or versatility
If comprehensive security monitoring is deployed across all systems, then security coverage is improved, but system complexity and false positives increase
Solution Approach 1:
The patent segments the comprehensive security monitoring into prioritized tiers based on risk assessment. Instead of treating all monitored systems equally, the framework divides them into high-priority and low-priority segments, allowing comprehensive coverage while managing complexity through structured categorization and focused attention on critical segments.
4Ease of operation
If manual incident response methodologies are used, then response flexibility is improved, but response time increases
Solution Approach 1:
The patent performs preliminary actions by pre-assessing incident priorities, pre-categorizing threats, and pre-planning response strategies based on risk levels. This preliminary processing occurs automatically before human responders engage, reducing the time required for manual assessment while preserving flexibility in executing predetermined response playbooks.
Data Source
AI summary
Disclosed are systems and methods for improving interactions with and between computers in a search system supported by or configured with search servers, applications or platforms. The systems interact to identify and retrieve data across platforms, which data can be used to improve the quality of results data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide an incident management and response software (IMRS) system that accelerates security incident detection and response. The IMRS provides an adaptive, event-driven workflow automation platform that can be customized to suit a large range of infrastructure environments and asset classes. The IMRS encompasses the management, automation and orchestration technologies applied in the detection and remediation of a computer network security incident (e.g., malware, advanced persistent threat, insider crime, denial of service attack, and the like).