ML-Based Change Control for Cloud Datacenter Risk
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Solution Overview
Problem
Current change control systems in cloud datacenters are prone to errors and inefficiencies due to manual and time-consuming analysis processes, which can lead to mistakes or gaps in protection, especially during urgent operations, and fail to effectively analyze compliance requirements and threat models in a timely manner.
Innovation Solution
An automated change control system utilizing machine-learning based behavioral analysis to determine the risk of proposed changes by generating graphs from job submissions, extracting information, and submitting it to a machine learning model for evaluation, which includes algorithms like artificial neural networks and ensemble random forests to accept, reject, or defer changes based on similarity scores and defined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis processes are used in change control systems, then compliance requirements and threat models can be reviewed, but the process becomes time-consuming and error-prone, especially during urgent operations
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated machine learning-based system. The machine learning model automatically evaluates job submissions against compliance requirements and threat models, eliminating the need for manual review while maintaining decision accuracy. This substitution of mechanical manual processes with automated intelligent systems directly resolves the contradiction between reliability and time loss.
Solution Approach 2:
The change control system performs self-service by automatically analyzing and evaluating job submissions using machine learning algorithms. The system autonomously determines whether changes should be approved, rejected, or deferred without requiring manual intervention, thereby reducing time loss while maintaining reliable compliance checking through automated self-evaluation capabilities.
2Productivity
If automated machine learning-based analysis is implemented, then decision-making speed is improved, but system complexity increases due to graph generation and information extraction processes
Solution Approach 1:
The patent segments the complex analysis system into distinct functional modules: graph generation module, information extraction module, and machine learning evaluation module. Each module performs a specific function in the automated analysis pipeline, making the overall complex system more manageable and maintainable while enabling high-speed automated decision-making through coordinated operation of these segmented components.
3Reliability
If manual review processes are used, then compliance requirements can be thoroughly checked, but urgent operations cannot be completed in time permitted
Solution Approach 1:
The patent substitutes manual compliance checking with automated machine learning-based compliance verification. The machine learning model continuously evaluates job submissions against compliance requirements in real-time, providing thorough compliance checking accuracy while enabling urgent operations to be completed within time permits through automated instantaneous evaluation rather than manual review processes.
Data Source
AI summary
Various embodiments of the present technology generally relate to systems, tools, and processes for change control systems. More specifically, some embodiments relate to machine learning-based systems, methods, and computer-readable storage media for job approvals, logging, and validation of critical functions and tasks based on compliance requirements, threat models, intended outcomes, rules, regulations, and similar restrictions or combinations thereof. Job approvals, rejections, and deferrals may be combined with machine learning techniques to conduct behavioral analysis in some implementations. The system disclosed herein provides for an improvement over existing change control methods requiring manual and time-consuming analysis. The system utilizes a combination of security, compliance, and auditing requirements along with machine-learning based behavior analysis of development, security, and operations functions and actions to determine risk, rejection, approval, or deferral of submissions in an automated manner.