SOC Threat Assignment Load Balancing by Analyst Response Profile
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Solution Overview
Problem
Existing cybersecurity operations centers face challenges in efficiently managing and balancing the workload of cybersecurity threats due to the evolving nature of cyber threats and the varying capabilities of analysts, leading to potential overload and inefficiencies in threat response.
Innovation Solution
A method involving analyzing the cybersecurity operations center (SOC) caseload history to produce an analyst threat response profile, augmenting it with resolution metrics, and assigning new threats based on analyst suitability, with the option to reassign existing caseloads to free up the best-suited analysts, utilizing machine learning for pedagogy planning to enhance analyst skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cybersecurity threats are manually assigned to analysts without load balancing, then assignment simplicity is maintained, but analyst workload becomes unbalanced and response efficiency decreases
Solution Approach 1:
The system performs self-service by automatically analyzing SOC caseload history, generating analyst threat response profiles, and making intelligent threat assignments without requiring manual intervention. The load balancing mechanism serves itself by continuously monitoring and redistributing workloads based on real-time analyst availability and threat characteristics.
Solution Approach 2:
The patent replaces manual mechanical assignment processes with an automated intelligent system that uses machine learning algorithms to analyze caseload patterns, generate threat response profiles, and make optimization decisions. This substitution transforms the manual workload management mechanism into an automated computational system.
2Reliability
If analysts are continuously assigned new threats without reassignment capability, then assignment speed is maintained, but analyst overload occurs and response quality deteriorates
Solution Approach 1:
The system implements dynamic workload management where threat assignments are not static but continuously adjusted based on changing analyst availability and threat priorities. The load balancing mechanism dynamically reassigns threats in real-time, allowing the system to adapt to fluctuating workloads and maintain optimal response quality.
Solution Approach 2:
The system uses feedback from SOC caseload history and analyst performance metrics to continuously improve threat assignment decisions. The generated threat response profiles incorporate historical data and performance feedback, enabling the system to learn from past assignments and optimize future allocations for both quality and speed.
3Measurement precision
If all threats are assigned to the most suitable analyst regardless of availability, then threat expertise matching is optimized, but analyst availability becomes a bottleneck
Solution Approach 1:
The system applies local quality by matching specific threat characteristics with corresponding analyst expertise profiles. Instead of uniform assignment rules, the system tailors assignments to the local requirements of each threat type and the specific skills of individual analysts, ensuring precise matching while considering availability constraints.
Solution Approach 2:
The system performs partial action by assigning threats to the most suitable available analyst rather than always attempting to find the single best match. When the ideal analyst is unavailable, the system partially satisfies the matching requirement by assigning to the next best available option, maintaining throughput while preserving expertise matching where possible.
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.