AI Vulnerability Remediation for First-Impression Network Threats

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Solution Overview

Problem

Existing communication networks face challenges in identifying and remediating vulnerabilities efficiently, leading to potential degradation or damage, and conventional manual remediation processes are time-consuming and expose networks to external threats.

Innovation Solution

Utilizing artificial intelligence and machine learning, particularly generative AI, to generate and implement near-instantaneous solutions for known and unknown vulnerabilities, with automation and user-controlled remediation options, and logging activities for audit tracking.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual vulnerability remediation processes are used, then labor costs and time consumption increase, but the complexity of the remediation system remains manageable

Engineering Contradiction:

Improvevulnerability remediation speedVSAvoidremediation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system enables automated self-service remediation by using AI models to autonomously analyze vulnerabilities, generate solutions, and implement fixes without human intervention. The orchestration system automatically coordinates between vulnerability detection, solution generation, and deployment, allowing the system to remediate itself

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical processes of vulnerability assessment and remediation are replaced with AI-driven automated systems. Machine learning models substitute human analysts, while automated orchestration replaces manual coordination, transforming the mechanical remediation process into an intelligent automated system

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of time

If AI and machine learning are deployed for automated vulnerability remediation, then remediation speed and efficiency improve, but operational costs and infrastructure requirements increase

Engineering Contradiction:

Improvetime to remediate vulnerabilityVSAvoidcomputational resource consumption

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

AI models are pre-trained on extensive vulnerability datasets and security knowledge bases before deployment. This preliminary training enables the models to quickly analyze and remediate vulnerabilities without requiring intensive computational resources during actual remediation operations, reducing real-time energy consumption

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements selective remediation by prioritizing critical vulnerabilities and applying AI analysis only where needed. Rather than continuously analyzing all system components, the orchestration system triggers AI models only when specific vulnerability conditions are detected, reducing unnecessary computational overhead

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If comprehensive vulnerability scanning and analysis are performed, then security detection capability improves, but system performance and processing time deteriorate

Engineering Contradiction:

Improvevulnerability detection accuracyVSAvoidnetwork operation efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system applies different levels of scanning intensity and analysis depth to different network segments and assets based on their criticality. High-value targets receive comprehensive analysis while less critical systems undergo lighter scanning, optimizing the balance between detection accuracy and operational efficiency

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system uses feedback from previous vulnerability detections and remediation outcomes to adjust scanning parameters and priorities. Historical data informs future scanning strategies, allowing the system to maintain high detection accuracy while reducing redundant scanning and improving overall efficiency

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12519821B2Apparatuses and methods for facilitating vulnerability remediations and solutions via artificial intelligence and machine learning

Publication Date: 2026.01.06 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Aspects of the subject disclosure may include, for example, obtaining a first indication of a first vulnerability in a first communication network or system, based on the obtaining of the first indication, analyzing the first vulnerability, based on the analyzing, determining that the first vulnerability is a vulnerability of first impression, based on the determining, generating a first solution to the first vulnerability utilizing machine learning, artificial intelligence, or a combination thereof, and based on the generating, implementing the first solution as part of the first communication network or system to address the first vulnerability in the first communication network or system. Other embodiments are disclosed.