Dynamic Constraint Templates for Cybersecurity Event Detection

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Solution Overview

Problem

In large computing environments, existing solutions face challenges in timely detection of unauthorized access, system errors, or data loss events due to the complexity of managing vulnerabilities and the need for continuous adaptation to changing landscapes.

Innovation Solution

The implementation of a dynamic AI/ML-based system that employs a Constraint Specification Matrix, combining human expertise and domain-specific knowledge with machine learning capabilities to identify vulnerability vectors and predict potential system vulnerabilities, allowing for continuous adaptation and optimization based on real-world experiences and emerging trends.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If AI/ML techniques are used to process large volumes of aggregated data, then the speed and efficiency of pattern identification is improved, but the complexity of the system increases

Engineering Contradiction:

Improvespeed of pattern identificationVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces a constraint specification template as an intermediary component that bridges AI/ML pattern detection and cybersecurity event identification. This template acts as a mediator that translates complex AI outputs into actionable security constraints, reducing the operational complexity while maintaining high-speed pattern identification capabilities.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments the cybersecurity detection process into distinct modules: data aggregation, AI/ML pattern identification, constraint specification template generation, and event detection. This segmentation allows each component to be optimized independently, managing overall system complexity while improving productivity through specialized processing.

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If dynamic constraint specification templates are generated based on parsed data, then the adaptability to changing cybersecurity landscapes is improved, but the processing time increases

Engineering Contradiction:

Improveadaptability to changing landscapesVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary parsing of cybersecurity event data and pre-generates constraint specification templates before actual threat detection occurs. This preliminary processing enables the system to adapt quickly to changing cybersecurity landscapes without significant processing delays during critical detection phases.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The constraint specification templates are designed as dynamic structures that can be generated and updated in real-time based on parsed data. This dynamic approach allows the system to adapt to evolving threats while managing processing time through efficient template generation algorithms that leverage existing data patterns.

Inventive Principle:

Principle #15Dynamics

3Measurement precision

If human expertise is combined with AI/ML systems for interpreting patterns, then the accuracy of threat identification is improved, but the operational complexity increases

Engineering Contradiction:

Improveaccuracy of threat identificationVSAvoidoperational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The AI/ML system performs self-service by automatically generating constraint specification templates based on parsed cybersecurity data, reducing the need for continuous human intervention. This automation maintains high accuracy in threat identification while significantly reducing operational complexity through self-generated, context-aware detection constraints.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback mechanisms where AI/ML-generated constraint templates are validated and refined based on actual cybersecurity event outcomes. This feedback loop improves the accuracy of threat identification over time while managing operational complexity through automated template refinement rather than manual reconfiguration.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250013742A1Systems, methods, and apparatuses for detecting cybersecurity events using centralized data aggregation and dynamic constraint specification templates in an electronic environment

Publication Date: 2025.01.09 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for detecting cybersecurity events using centralized data aggregation and dynamic constraint specification templates in an electronic environment. The present disclosure is configured to identify at least one of a malfeasant event or a potential malfeasant event; parse the data of the malfeasant event or the potential malfeasant event; generate a primary dynamic constraint specification template comprising a base set of parameters; identify at least one secondary malfeasant event or at least one secondary potential malfeasant event; parse the secondary data of the least one secondary malfeasant event or the at least one secondary potential malfeasant event; and generate at least one secondary dynamic constraint specification template comprising a secondary set of parameters, wherein the at least one secondary dynamic constraint specification template is a modification of the primary dynamic constraint specification template.