Third-Party Integration Risk Mitigation via Normalized Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Integrating third-party computing systems into first-party systems poses risks of data breaches and loss due to unauthorized access, necessitating effective risk management and mitigation strategies.
Innovation Solution
A risk management and mitigation computing system that analyzes risks by accessing tenant computing system risk data, determines normalized risk scores, and generates control recommendations using rules-based or machine-learning models to implement controls, thereby mitigating identified risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If third party computing system functionality is integrated into first party computing system, then computing functionality and versatility are improved, but data security and risk of data breaches worsen
Solution Approach 1:
The system performs preliminary risk analysis and control determination before integration occurs. It accesses risk data from tenant computing systems, identifies particular risks, determines normalized risk scores, and selects appropriate controls in advance to mitigate risks before the actual integration takes place.
Solution Approach 2:
The system introduces an intermediary risk management layer between the first party computing system and third party computing system. This intermediary component analyzes risks, determines controls, and facilitates safe integration without directly exposing the core systems to each other, thereby reducing data breach risks while maintaining functionality.
2Object-affected harmful factors
If risk analysis and control implementation are performed, then data security is improved, but system complexity and processing time worsen
Solution Approach 1:
The system uses a universal risk analysis framework that can be applied across multiple tenant computing systems and different third party integrations. The same core processes (accessing risk data, identifying risks, determining normalized scores, selecting controls) serve multiple purposes, reducing overall system complexity through standardization.
Solution Approach 2:
The system transforms diverse risk data from multiple sources into a standardized normalized risk score format. By changing the parameter representation to a common scale, it simplifies comparison and control selection across different risks, reducing processing complexity while maintaining comprehensive security analysis.
3Object-affected harmful factors
If multiple controls are implemented to mitigate risks, then data protection is improved, but implementation time and resource consumption worsen
Solution Approach 1:
The system determines an appropriate set of controls based on the normalized risk scores and tenant-specific requirements. Rather than implementing all possible controls, it selects the partial set that is most effective for the identified risks, optimizing the balance between protection and implementation time.
Solution Approach 2:
The system incorporates feedback loops where control effectiveness is evaluated and control adoption data is updated. This feedback mechanism allows the system to learn from previous implementations and refine future control selections, reducing implementation time through improved decision-making based on historical performance data.
Data Source
AI summary
In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for integrating third party computing system functionality into a first party computing system by providing a risk management and mitigation computing system configured to analyze a risk of integrating the functionality provided by the third party computing system and facilitating implementation of one or more data-related controls that include performing computer-specific operations to mitigate and/or eliminate the identified risks. For example, the risk management and mitigation computing system can access risk data in tenant computing systems to determine a risk score related to the integration of the third party computing system functionality based on risks determined during prior integrations of the third party computing system functionality by other tenant computing systems. The risk management and mitigation computing system can generate a recommended control when integrating the third party computing system functionality.