Automated Risk Scoring for Incident Prioritization
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Solution Overview
Problem
Cybersecurity teams face challenges in effectively prioritizing and addressing incidents due to the high volume of alerts, often relying on expertise rather than data-driven approaches, which can lead to inefficiencies in resource allocation and threat mitigation.
Innovation Solution
A system and method for generating risk scores based on actual loss events, using a dataset that includes breach data and insurance data to determine attack tactic risk scores, incident risk scores, and asset risk scores, thereby providing a data-driven approach to prioritize incidents and allocate resources effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security operations analysts manually determine whether incidents are malicious, then expertise-based judgment can be applied, but the high quantity of incidents makes this approach inefficient and challenging
Solution Approach 1:
The patent replaces manual analyst judgment (mechanical human decision-making) with an automated risk scoring system that uses machine learning models and historical loss event data to objectively prioritize incidents. This substitution enables both high accuracy through sophisticated algorithms and high throughput through automation, resolving the contradiction between precision and productivity.
Solution Approach 2:
The patent introduces an intermediary risk scoring system that processes incidents between detection and response phases. This intermediary layer automatically evaluates incidents using multiple data sources and algorithms, providing prioritized outputs that guide analyst attention without requiring manual evaluation of every incident, thus improving both accuracy and throughput.
2Reliability
If organizations focus on investigating all incidents thoroughly, then comprehensive security coverage is achieved, but resource allocation becomes inefficient due to the high volume of alerts
Solution Approach 1:
The patent applies local quality by providing different levels of analysis and attention to different incidents based on their risk scores. High-risk incidents receive comprehensive investigation resources, while low-risk incidents receive automated handling or minimal attention. This differentiated approach maintains overall security reliability while optimizing resource allocation and reducing time loss.
Solution Approach 2:
The patent implements partial action by focusing investigative resources on the most critical incidents rather than treating all incidents equally. The risk scoring system identifies a subset of high-priority incidents that warrant thorough investigation, allowing organizations to achieve sufficient security coverage without wasting resources on low-risk false positives, thus improving resource allocation efficiency.
3Productivity
If data-driven approaches are used to prioritize incidents, then resource allocation efficiency improves, but the complexity of processing and analyzing multiple data sources increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modular components: data collection from multiple sources, data normalization and cleaning, feature extraction, risk scoring calculation, and incident prioritization output. Each module handles a specific aspect of the data flow, making the overall system more manageable and maintainable while achieving efficient resource allocation through automated analysis.
Data Source
AI summary
In one embodiment, a method includes determining an attack tactic risk score for one or more attack tactics based on a dataset of actual loss events and determining an incident risk score for an incident based on the one or more attack tactic risk scores. The method also includes determining a priority value for an asset. The asset is associated with the incident. The method further includes generating an asset risk score for the asset based on the priority value of the asset and the incident risk score.