Multi-Attestation Queue Framework for Fragmented Access Regimes
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Solution Overview
Problem
Managing digital environments with multiple endpoints is complex due to the scale and partitioning of digital assets, leading to inefficiencies in security systems, increased costs, and delays in accessing critical documents, with existing frameworks struggling with scalability, accuracy, and integration across different service providers.
Innovation Solution
A multi-process attestation framework utilizing a Python-based application with queuing theory principles to manage access control, normalize fragmented objects, and ensure timely processing through a task manager that vertically and horizontally scales resources, reducing metadata processing by over 90% through data manipulation and normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security systems are implemented to manage digital assets across multiple endpoints, then security control is improved, but system complexity and management costs increase significantly
Solution Approach 1:
The system segments the digital environment into multiple endpoints (cloud services, on-premises systems, devices) and implements separate attestation modules for each endpoint type. Each endpoint can be managed independently with its own security policies, reducing the complexity of managing a monolithic security system across the entire digital environment.
Solution Approach 2:
The patent introduces an intermediary attestation framework that sits between endpoints and digital assets. This framework translates diverse endpoint authentication mechanisms into a unified attestation model, simplifying security management by abstracting away the complexity of individual endpoint security systems.
2Reliability
If comprehensive security systems are deployed to protect digital assets, then security coverage is improved, but access speed and user productivity deteriorate due to delays
Solution Approach 1:
The system performs attestation verification in advance before digital assets are accessed. By pre-validating endpoint security postures and user credentials, the system prepares authentication states ahead of time, so that when users need to access assets, the verification is already complete or can be quickly finalized, minimizing access delays.
Solution Approach 2:
The attestation framework establishes continuous security monitoring and validation across endpoints. Once an endpoint is attested, the security validation continues in the background, allowing users to access assets with minimal interruption. The system maintains continuous attestation states rather than requiring repeated full authentication cycles.
3Adaptability or versatility
If multiple service providers manage different aspects of the digital environment, then service specialization is improved, but integration and cooperation between partitions deteriorate
Solution Approach 1:
The attestation framework implements a universal attestation model that can work across multiple service providers and endpoint types. The same core attestation mechanisms and data structures are used whether validating cloud services, on-premises systems, or devices, enabling different specialized providers to cooperate through a common interface without requiring provider-specific integration logic.
Data Source
AI summary
Technology is provided for managing access regimes. A method includes generating a first set of tasks to retrieve target properties from a data element as a plurality of fragmented objects, and assigning the tasks to a queue. Nodes perform tasks in the queue. The method includes generating a second set of tasks to process the plurality of fragmented objects into a normalized data structure, and assigning them to the queue. At least some nodes are configured to normalize a respective fragmented object of the second set of tasks into the normalized data structure, and update the queue. In some examples, the normalization is based on a data model that uses a machine learning engine. The method includes generating a third task to generate a final normalized data structure for the data element, and generating the final normalized data structure by aggregating the normalized fragmented objects processed by the nodes.