Lifecycle Drift Analysis for DevSecOps Deviation Detection
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Solution Overview
Problem
Existing DevSecOps methodologies are prone to manual errors, overlook critical deviations, and lack comprehensive tools for identifying and addressing issues such as undocumented changes, misconfigurations, and data corruption, leading to inefficiencies and deployment risks.
Innovation Solution
A system and method that collects and analyzes specification documentation and drift data throughout the product lifecycle, identifies anomalies, assigns risk scores, and provides content-based recommendations for remediation, displayed in a dashboard.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual DevSecOps methodologies are used, then implementation simplicity is maintained, but manual errors increase and critical deviations are overlooked
Solution Approach 1:
The patent introduces an intermediary automated monitoring system that acts as a mediator between manual DevSecOps processes and deviation detection. This system includes drift detection agents, anomaly detection models, and a centralized monitoring platform that automatically identifies and analyzes deviations without requiring manual intervention, thereby improving reliability while managing complexity through modular architecture
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Specifically, it substitutes human manual monitoring and analysis with automated drift detection algorithms, anomaly detection models, and machine learning-based analysis systems that continuously monitor system state and identify deviations automatically, eliminating manual errors while maintaining operational simplicity
2Measurement precision
If comprehensive automated monitoring is implemented, then deviation detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the comprehensive monitoring system into distinct modular components: drift detection agents deployed at individual system levels, data collection modules, anomaly detection models, analysis engines, and reporting interfaces. Each component performs a specific function with defined precision, allowing high measurement precision in anomaly detection while managing overall system complexity through modular, independently deployable units
Solution Approach 2:
The patent introduces intermediary layers between raw data collection and final analysis, including drift detection agents that filter and preprocess data, anomaly detection models that identify patterns, and analysis engines that interpret findings. These intermediaries enhance measurement precision by systematically processing data through multiple stages while managing complexity through layered architecture
3Reliability
If continuous monitoring throughout product lifecycle is performed, then deployment risks are minimized, but time and computational resources are consumed
Solution Approach 1:
The patent implements preliminary action by deploying drift detection agents and establishing baseline system states before product deployment. The system pre-configures monitoring parameters, collects initial specification data, and prepares anomaly detection models in advance. This preliminary setup enables rapid continuous monitoring during the product lifecycle without consuming excessive time during critical deployment phases, as the foundational monitoring infrastructure is already in place
Solution Approach 2:
The patent maintains continuous monitoring throughout the product lifecycle by implementing persistent drift detection agents that continuously collect data, update drift metrics, and feed information to anomaly detection models. This continuous useful action ensures deployment safety through constant surveillance while optimizing resource consumption by maintaining steady-state monitoring operations rather than intermittent intensive scanning
Data Source
AI summary
A computer-implemented method including: collecting, by a computing device, specification documentation of a product and drift data of the product during a product lifecycle; analyzing, by the computing device, the specification documentation and the drift data of the product to identify anomalies between the specification documentation and the drift data of the product during different stages of the product lifecycle; computing, by the computing device, a score of each of the identified anomalies; recommending, by the computing device, solutions to fix selected anomalies based on their score and by using content-based recommendations; and displaying the recommended solutions to an end-user in a dashboard.