ML-Based Credential Authorization Advisor
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Solution Overview
Problem
Conventional IT access systems lack automation in credential management, leading to inefficiencies and delays in granting access to IT infrastructure, which impacts technical operations, business targets, and financial constraints.
Innovation Solution
The implementation of machine learning models that collect data from various sources, including human resource databases and asset management databases, to automatically generate and provide access credentials to users requesting access to IT infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual credential management processes are used, then security control and authorization are maintained, but time consumption and administrative effort increase significantly
Solution Approach 1:
The system enables automated self-service credential generation by allowing users to request credentials through a portal, which are then automatically evaluated against security policies and generated without manual administrator intervention. This maintains security through automated policy enforcement while eliminating time-consuming manual approval processes.
Solution Approach 2:
The patent replaces manual mechanical credential management processes with an automated machine learning-based system. The ML models automatically evaluate user credentials against security policies, replacing human administrators' manual review and approval processes, thereby maintaining security control while dramatically reducing time consumption.
2Reliability
If strict security policies and extensive controls are implemented, then security breaches are prevented, but credential approval time increases
Solution Approach 1:
The system replaces manual security policy review processes with machine learning models that automatically evaluate credentials against predefined security policies. This substitution enables rapid automated evaluation while maintaining strict security enforcement, resolving the contradiction between security policy enforcement and approval speed.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between credential requests and security policy evaluation. These ML models act as automated mediators that quickly assess whether credentials comply with security policies, enabling fast approval decisions without compromising security enforcement.
3Productivity
If automated machine learning models are implemented, then processing speed and operational efficiency improve, but system complexity increases
Solution Approach 1:
The patent implements a universal credential management system that handles multiple credential types (user credentials, device credentials, service credentials) through a single automated ML-based platform. This multi-functional approach improves processing speed across all credential types while consolidating system complexity into one unified system rather than multiple separate systems.
Data Source
AI summary
Techniques are provided for selectively granting access credentials through the use of a machine learning model. Embodiments include collecting data from one or more sources related to user access of an information technology (IT) infrastructure. Based on the collected data, a machine learning model is created for authenticating a request from a client device to access the computer system within the IT infrastructure based on the collected data, based on the machine learning model. An access credential is generated upon processing the user identifier as an input to the machine learning model, and the access credential is provided to the client device.