Cognitive Auditing of Client-Bound Data via Predictive Validation

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Solution Overview

Problem

Existing systems lack effective validation of client-bound data, leading to unauthorized data access and exposure of sensitive information due to misuse of authentication tokens and SQL injection attacks.

Innovation Solution

A computing platform intercepts user requests, generates predicted response data using a machine learning model trained on historical data, and validates actual response data against the predicted data to ensure appropriate data is returned, preventing unauthorized access and alerting administrative devices of discrepancies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If data is returned to client without checks on client bound data, then processing speed is improved, but security deteriorates leading to unauthorized data access

Engineering Contradiction:

Improveprocessing speedVSAvoidsecurity

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The system performs preliminary validation of client-bound data by generating predicted response data using a machine learning model trained on historical data before the actual data processing occurs. This predicted response serves as a reference for subsequent validation, enabling security checks to be performed efficiently without significantly impacting processing speed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary validation layer that compares actual response data against predicted response data generated by the machine learning model. This intermediary comparison mechanism enables security validation without requiring complete reprocessing of data, thus maintaining processing speed while improving security.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If authentication tokens are validated without cognitive analysis, then ease of operation is improved, but reliability deteriorates due to token misuse attacks

Engineering Contradiction:

Improveease of operationVSAvoidauthentication reliability

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system implements feedback by continuously comparing actual response data against predicted response data generated from historical patterns. This feedback mechanism enables the system to detect anomalies in authentication token usage, such as tokens being used in out-of-context scenarios, while maintaining ease of operation for legitimate users.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The machine learning model is trained in advance on historical authentication data to establish baseline patterns of legitimate token usage. This preliminary training enables the system to quickly validate authentication tokens during operation without complex real-time analysis, maintaining ease of operation while improving authentication reliability.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If SQL injection attacks are not detected, then productivity is improved, but harmful factors increase due to data exposure

Engineering Contradiction:

ImproveproductivityVSAvoiddata exposure

Core Design Contradiction:

ProductivityVSObject-generated harmful factors

Solution Approach 1:

The patent introduces an intermediary validation layer that compares actual response data against predicted response data generated by the machine learning model. This intermediary comparison mechanism detects SQL injection attacks and other malicious patterns without requiring complete reprocessing of data, thus maintaining productivity while preventing data exposure.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary validation by generating predicted response data using a machine learning model trained on historical data before the actual data processing occurs. This predicted response serves as a reference for subsequent validation, enabling security checks to be performed efficiently without significantly impacting processing speed.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11991188B2Cognitive auditing of client bound data

Publication Date: 2024.05.21 BANK OF AMERICA CORP

AI summary

Aspects of the disclosure relate to cognitive auditing of client bound data. A computing platform may intercept a request from a user device to an application server. The computing platform may identify content requested by the user device and generate predicted response data based on the requested content. The computing platform may transmit the request to the application server. The computing platform may receive, from the application server, actual response data. The computing platform may validate the actual response data by comparing the actual response data with the predicted response data. Based on comparing the actual response data with the predicted response data, the computing platform may identify whether the actual response data matches the predicted response data. Based on identifying that the actual response data matches the predicted response data, the computing platform may send the actual response data to the user device.