Predictive Access Control Lists Using Content and User Profiles

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Solution Overview

Problem

Updating and maintaining access control lists (ACLs) in computer systems is time-consuming and labor-intensive.

Innovation Solution

A predictive engine using a transformer model dynamically generates ACLs based on user profiles and document content, with user feedback for training, to improve accuracy over time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual methods are used to update and maintain access control lists, then flexibility and control are maintained, but the process becomes time-consuming and labor-intensive

Engineering Contradiction:

ImproveACL update efficiencyVSAvoidTime for ACL maintenance

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables self-service automation where the predictive engine automatically generates and updates ACLs based on user profiles and document content analysis, eliminating the need for manual ACL management while maintaining security policies. The system serves itself by continuously learning from user feedback and improving predictions without human intervention.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes of ACL creation and maintenance with an automated predictive engine using machine learning algorithms. The transformer model processes user profiles and document content to automatically determine appropriate access controls, substituting human labor with intelligent automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated methods are used to generate ACLs, then time and labor are reduced, but accuracy and reliability may be compromised

Engineering Contradiction:

ImproveACL generation speedVSAvoidACL accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements a feedback mechanism where user corrections and verifications of generated ACLs are fed back into the predictive engine. This continuous feedback loop allows the transformer model to learn from actual user decisions and improve the accuracy of future predictions, ensuring reliability while maintaining automation benefits.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The predictive engine performs preliminary analysis of user profiles and document content to generate draft ACLs before final implementation. This preliminary action allows for review and verification while capturing the efficiency benefits of automation, balancing speed with accuracy through staged processing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12585808B2Systems and methods for content based access control

Publication Date: 2026.03.24 SAP SE

AI summary

Embodiments of the present disclosure include techniques for controlling access to electronic content. In one embodiment, a user generates content in an electronic document. The system retrieves the content and a profile for the user. A predictive engine determines an access control list comprising a plurality of entries based on the content and the profile. The access control list may be presented to the user, and the system receives a verification from the user of the plurality of entries in the access control list.