SOC Threat Assignment Using Analyst Load Balancing Profiles
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Solution Overview
Problem
Cybersecurity operations centers face challenges in efficiently managing and balancing the workload of cybersecurity analysts due to the rapid evolution of cyber threats and the constant need for vigilance, as existing measures like preventive software updates are inadequate, and analysts often become overwhelmed with caseloads, leading to inefficiencies and potential gaps in threat response.
Innovation Solution
A cybersecurity operations center load balancing system that analyzes the SOC caseload history to produce analyst threat response profiles, augmented with resolution metrics, and assigns new threats based on analyst suitability, reassigning existing cases to ensure optimal workload distribution and developing pedagogy plans using machine learning to enhance analyst capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If analysts manually manage cybersecurity threats without automated load balancing, then individual analyst expertise can be utilized, but analyst workload becomes unbalanced and response efficiency decreases
Solution Approach 1:
The system enables self-service through automated load balancing that autonomously monitors analyst workloads, evaluates threat characteristics, and reassigns cases without human intervention. The load balancing system automatically detects workload imbalances and redistributes threats based on analyst capacity and expertise, freeing analysts from manual workload management while maintaining optimal threat response efficiency
Solution Approach 2:
The system dynamically adjusts workload distribution by changing key parameters including analyst workload capacity, threat severity levels, and response time requirements. These parameter changes enable the system to adapt to varying threat landscapes and analyst availability, optimizing both productivity and operational ease through data-driven workload allocation
2Reliability
If preventive measures like software updates are used, then system security is maintained, but they are inadequate against rapidly evolving cyber threats
Solution Approach 1:
The system performs preliminary action by proactively analyzing threat patterns and preparing response strategies before attacks materialize. The load balancing system pre-evaluates threat characteristics and pre-assigns cases to appropriately skilled analysts, enabling faster response to evolving threats while maintaining reliable security through advance preparation
Solution Approach 2:
The system implements continuous feedback loops that monitor threat evolution, analyst performance, and workload distribution in real-time. This feedback enables dynamic adjustment of security strategies and workload allocation, allowing the system to adapt to rapidly changing threats while maintaining reliable protection through data-driven decision-making
3Reliability
If more analysts are hired to handle increased threat volume, then threat coverage improves, but operational costs and management complexity increase
Solution Approach 1:
The system achieves universality by creating a multi-functional load balancing platform that simultaneously performs workload distribution, analyst performance evaluation, training needs analysis, and resource optimization. This single system handles multiple operational functions that would otherwise require separate management processes, improving threat coverage while reducing operational complexity
Solution Approach 2:
The system optimizes resource utilization by dynamically changing parameters such as analyst workload capacity, threat prioritization levels, and skill-matching criteria. These parameter adjustments enable existing analysts to handle increased threat volumes efficiently without requiring proportional increases in headcount, thereby improving threat coverage while controlling operational complexity
Data Source
AI summary
Disclosed techniques include cybersecurity operations center load balancing. A cybersecurity security operations center (SOC) caseload history is accessed. Triage results from the SOC caseload history are analyzed on a computer platform to produce an analyst threat response profile. The analyst threat response profile is augmented with threat response resolution metrics. The threat response resolution metrics are updated with a subjective rating. The subjective rating is supplied by management, peers, or machine learning. Notification of a new cybersecurity threat is received across a cybersecurity network by the SOC. The new cybersecurity threat is assigned to a specific analyst, based on the augmented analyst threat response profile. The assigning is further based on weighting of threat severity, threat complexity, and analyst availability. An existing SOC caseload is reassigned to increase availability of the specific analyst.