Dynamic Access Control via Workflow Context ML
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Solution Overview
Problem
Manual management of dynamic access control in security ecosystems is time-consuming, resource-intensive, and prone to errors, especially in environments where user access levels change frequently, such as in schools, hospitals, or police stations.
Innovation Solution
A system utilizing a trained machine learning model to dynamically assign access privileges based on user workflows, monitoring activity to determine necessary access and granting privileges only when required, thereby improving efficiency and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual management of access control is used, then security personnel can control access to facilities, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system enables self-service access control by automatically granting access rights to users based on their workflow context and job requirements. The machine learning model analyzes user activity and dynamically assigns access privileges without requiring manual security personnel intervention, thus maintaining security while eliminating time-consuming manual processing.
Solution Approach 2:
The system performs preliminary actions by pre-configuring access rights based on predicted workflow needs. The machine learning model anticipates which access privileges users will require based on their current tasks and job roles, granting access before users need it, thereby reducing wait time while maintaining appropriate security controls.
2Reliability
If manual access management is implemented, then access decisions can be made by security personnel, but the process requires in-depth knowledge of facilities and user job requirements
Solution Approach 1:
The system replaces the mechanical system of manual security personnel decision-making with an automated machine learning-based system. The ML model processes user workflow data, facility information, and job requirements to automatically determine appropriate access rights, eliminating the need for human experts to possess deep knowledge of all facilities and user roles while maintaining accurate access control decisions.
3Stability of the object's composition
If static access control is used, then user permissions remain consistent, but the system cannot adapt to dynamic security ecosystems where new users and tasks are frequently added
Solution Approach 1:
The system implements dynamic access control where user permissions are not fixed but continuously adjusted based on current workflow context. The machine learning model monitors user activity and automatically updates access rights in real-time, allowing the system to adapt to new users, tasks, and changing security requirements while maintaining consistent access control policies through learned patterns from historical data.
Data Source
AI summary
A computer-implemented method comprises monitoring activity associated with a user, determining, by a trained machine learning model using the monitored activity of the user, that the user will need to access an asset that the user does not currently have access to, the machine learning model trained with a plurality of previous workflows completed by previous users and associated access privileges required for the previous workflows; and assigning an access privilege to the user, wherein the user is thereafter able to access to the asset.