Cloud Remediation Engine Automating Security Fixes

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Solution Overview

Problem

Cloud-based applications face security vulnerabilities when they are due for security patches or updates, and when corresponding cloud-based images are not refreshed, leading to potential cyberattacks.

Innovation Solution

A system for automating remediation solutions using context-based machine learning, which receives indications of security vulnerabilities, performed remediation steps, and planned remediation steps, applies a machine learning model to generate a recommended remediation procedure, and transmits this procedure to address subsequent instances of the vulnerability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual remediation processes are used for security vulnerabilities, then flexibility in handling different vulnerability types is maintained, but remediation delays occur and security response time is reduced

Engineering Contradiction:

Improvesecurity response timeVSAvoidremediation delay

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables automated self-service remediation by using machine learning models to autonomously analyze security vulnerability data, determine appropriate remediation steps, and execute fixes without requiring constant human intervention. The ML model processes vulnerability information and automatically generates and applies remediation scripts, allowing the system to service itself and respond rapidly to security threats.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary actions by pre-processing security vulnerability data and pre-determining remediation strategies before actual security incidents occur. The machine learning model is trained in advance on historical vulnerability data and remediation patterns, enabling it to quickly recommend and apply appropriate fixes when new vulnerabilities are detected, thereby reducing remediation delays.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If multiple interfaces are provided for accessing remediation options, then user accessibility is improved, but system complexity increases and resources are consumed

Engineering Contradiction:

Improveuser accessibilityVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system implements a universal interface that consolidates multiple remediation access points into a single unified entry. This single interface provides comprehensive access to all remediation options, vulnerability information, and system controls, eliminating the need for multiple separate interfaces while maintaining full functionality and user accessibility.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system merges multiple disparate interfaces and access points for remediation options into one integrated interface. By combining vulnerability detection, analysis, remediation recommendation, and execution functions into a unified access point, the system reduces complexity while preserving ease of operation and resource efficiency.

Inventive Principle:

Principle #5Merging (Combining)

3Productivity

If automated remediation is implemented, then remediation speed is improved, but accuracy of remediation selection may be reduced without proper context analysis

Engineering Contradiction:

Improveremediation speedVSAvoidremediation selection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements feedback mechanisms where the machine learning model continuously learns from the outcomes of automated remediation actions. By analyzing whether remediation steps successfully resolved vulnerabilities and gathering feedback on system responses, the model refines its decision-making accuracy over time, ensuring that automated remediation maintains high precision while operating at fast speeds.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system replaces manual mechanical analysis and decision-making processes with machine learning-based automated analysis. The ML model substitutes human experts' contextual analysis capabilities by processing vulnerability data, identifying patterns, and selecting remediation actions automatically, thereby maintaining high accuracy in remediation selection while dramatically increasing remediation speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250036758A1Building and providing a remediation library for cloud-based applications

Publication Date: 2025.01.30 CAPITAL ONE SERVICES LLC

AI summary

In some implementations, a remediation engine may receive an indication of a current instance of a security vulnerability associated with a cloud-based application. The remediation engine may determine, using a first machine learning model, a previous ticket associated with resolving a previous instance of the security vulnerability. The remediation engine may generate, using a second machine learning model, an automated script associated with resolving the security vulnerability. The remediation engine may transmit instructions for a user interface (UI) indicating the current instance of the security vulnerability. The remediation engine may receive an indication of an interaction with the UI. The remediation engine may transmit instructions for an overlay, indicating the previous ticket and the automated script, in response to the indication of the interaction with the UI.