Process Mining for Automated Vulnerability Inference
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Solution Overview
Problem
Current methods for operational process management require substantial manual effort to elucidate and interpret process ecosystems, and there is a need for automated systems to infer organizational behavior, decision-making processes, and vulnerabilities from discovered process ecosystems.
Innovation Solution
The system collects event logs, measures transition times, calculates activity capacity, and generates natural language output to infer behavior and vulnerabilities, allowing for real-time identification of performance bottlenecks and potential improvements, while reducing the need for manual input and facilitating autonomous process adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated systems are used to infer organizational behavior and vulnerabilities from process ecosystems, then manual effort and time required for process analysis is reduced, but system complexity and computational requirements increase
Solution Approach 1:
The patent replaces manual mechanical analysis of process ecosystems with automated computational systems. Machine learning algorithms and process mining tools automatically extract insights from event logs and process data, substituting human analysts' manual examination with algorithmic processing that achieves similar or superior analysis speed and consistency.
Solution Approach 2:
The system performs self-service analysis by automatically inferring organizational behavior, decision-making processes, and vulnerabilities from process data without requiring continuous human intervention. The automated system monitors processes, detects anomalies, and generates insights autonomously, reducing the need for manual oversight while maintaining analytical capabilities.
2Measurement precision
If detailed manual analysis of process ecosystems is performed, then accuracy and depth of process understanding is improved, but productivity and speed of analysis deteriorates
Solution Approach 1:
Manual mechanical analysis is replaced with automated computational algorithms that process large volumes of process data rapidly. Machine learning models and process mining algorithms automatically extract detailed insights, measure process performance, and identify vulnerabilities at high speed, achieving both accuracy and productivity simultaneously.
Solution Approach 2:
The system enables continuous automated analysis of process ecosystems without interruption. Unlike manual analysis that operates in discrete batches, the automated system continuously monitors processes, updates models, and generates insights in real-time, maintaining both high accuracy through continuous learning and high productivity through uninterrupted operation.
3Productivity
If automated extraction of parameters from process data is implemented, then productivity and speed of insight generation is improved, but measurement precision and accuracy of inferred behavior may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where automated parameter extraction results are continuously validated and refined. Process mining algorithms automatically adjust their inference models based on feedback from actual process outcomes, ensuring that speed of insight generation does not compromise measurement precision. The feedback loop allows rapid iteration while maintaining accuracy.
Solution Approach 2:
The patent uses sophisticated machine learning algorithms that substitute manual analytical judgment with computationally intensive but highly accurate automated inference. These algorithms process complex process data patterns at high speed while maintaining precision through advanced statistical models and neural networks that can capture nuanced behavioral patterns.
Data Source
AI summary
Systems and methods for process models to determine systems behavior and vulnerabilities are provided. In one embodiment, a method comprises collecting event logs from monitoring systems communicatively coupled to a computing device, each event log indicating an event occurring at a given time at a given activity within a process, measuring transition times between activities of the process from the event logs, calculating, from the measured transition times, a capacity of an activity of the activities, inferring behavior and vulnerabilities of the process based on one or more of the measured transition times and the capacity, and generating natural language output indicating the inferred behavior and vulnerabilities of the process. Further, simulations of the process are performed with statistical data regarding the event logs as input. In this way, aspects of a process such as an operational process in need of attention or vulnerable to external attacks may be rapidly identified and actions for resolution may be automatically recommended.