Software Change Annotation Across Environments for Anomaly Detection
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Solution Overview
Problem
Existing systems struggle to efficiently monitor and manage computing environments across multiple platforms, including cloud environments, for security, compliance, and anomaly detection, due to the complexity and heterogeneity of these environments.
Innovation Solution
A data platform is deployed to ingest, process, and analyze data from various compute assets using agents that collect and report activities, generating polygraphs to identify anomalies and deviations from baselines, with data processing resources performing real-time analytics and user interface resources providing insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If agents collect and report activities from compute assets across multiple cloud environments, then monitoring coverage and detection capability are improved, but data volume and processing complexity increase
Solution Approach 1:
The system extracts only the essential activity information from compute assets using agents, filtering out redundant data while capturing key events such as resource creation, modification, and deletion. This selective extraction reduces data volume while maintaining anomaly detection capability.
Solution Approach 2:
The system performs preliminary processing of collected data by generating polygraphs that represent expected behavior patterns before actual anomaly detection occurs. This pre-processing organizes data into meaningful structures, reducing the complexity of subsequent analysis.
2Measurement precision
If polygraphs are generated to represent expected behavior patterns, then anomaly detection accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system segments the computation by generating polygraphs at different levels of abstraction - from individual compute asset behaviors to broader environmental patterns. This segmentation allows the system to process data in manageable chunks, reducing overall computational burden while maintaining detection accuracy.
Solution Approach 2:
The system generates polygraphs that capture expected behavior patterns with sufficient detail for anomaly detection without over-processing every possible data point. This partial action approach creates adequate representations of normal behavior while conserving computational resources.
3Speed
If real-time analytics are performed on activity data, then response time to threats is improved, but processing speed requirements and system complexity increase
Solution Approach 1:
The system performs preliminary polygraph generation and baseline establishment in advance, so that when anomalies occur, the framework is already prepared to compare new data against established patterns. This eliminates the need for complex real-time pattern learning, reducing processing complexity while maintaining fast response times.
Solution Approach 2:
The system continuously compares actual activity data against generated polygraphs and provides feedback when deviations indicate anomalies. This feedback mechanism enables real-time detection using pre-computed reference data, avoiding the need for complex real-time analysis while maintaining rapid response capability.
4Adaptability or versatility
If the system monitors diverse computing environments across multiple cloud providers, then coverage and versatility are improved, but heterogeneity and integration complexity increase
Solution Approach 1:
The system employs a universal polygraph generation framework that can process activity data from multiple cloud providers and compute asset types using the same underlying principles. This multi-functionality approach allows diverse environments to be monitored through a unified system, reducing integration complexity despite the variety of monitored platforms.
Data Source
AI summary
Annotating changes in software across computing environments, including: monitoring a software development and deployment pipeline; annotating a detected change in software with evidence providing information about one or more aspects of the detected change in software; and referring to the annotations for an unexpected change upon detecting the unexpected change in an environment that is outside of the software development and deployment pipeline.