NLP-Assisted Data Protection Policy Assignment
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Solution Overview
Problem
In data protection environments, manually assigning data protection policies to numerous entities is cumbersome and impractical, especially when thousands of entities are involved, as administrators lack knowledge of organizational policies and regulatory requirements, leading to scalability issues and inconsistent policy implementation.
Innovation Solution
The system uses natural language processing (NLP) to analyze user intentions, correlate them with industry standards and regulatory requirements, and automatically generate and assign data protection policies to protected entities, allowing users to specify their intentions through text or voice inputs, thereby simplifying the policy assignment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual policy assignment is used for each protected entity, then policy customization and control are improved, but scalability and administrative burden deteriorate when thousands of entities are involved
Solution Approach 1:
The system enables self-service by allowing protected entities to automatically generate and assign their own data protection policies based on metadata and organizational policies, without requiring manual administrator intervention for each entity. This resolves the contradiction by enabling scalability through automation while maintaining policy control through structured policy templates and metadata-driven configurations.
Solution Approach 2:
The system performs preliminary action by pre-defining organizational policies, metadata schemas, and policy templates in advance. These pre-configured elements enable automatic policy generation when new entities are created, eliminating the need for manual policy assignment while ensuring consistency with organizational requirements. This approach maintains control through pre-established policy frameworks while achieving scalability through automated policy instantiation.
2Productivity
If responsibility for setting data protection policy is delegated to the person who created the protected entity, then administrative workload is reduced, but knowledge of organizational policies and regulatory requirements is typically lacking
Solution Approach 1:
The system introduces an intermediary layer consisting of metadata schemas, organizational policy templates, and automated policy generation logic. This intermediary translates user intent expressed through metadata into compliant data protection policies, bridging the gap between creators who lack policy knowledge and organizational compliance requirements. The intermediary ensures that even users without policy expertise can create compliant policies through structured metadata input.
Solution Approach 2:
The system implements feedback mechanisms where the automated policy generation process validates user-provided metadata against organizational policies and regulatory requirements, providing guidance and corrections when compliance issues are detected. This feedback loop ensures that policies generated by non-experts still meet organizational standards, maintaining reliability while enabling broader participation in policy creation.
3Productivity
If NLP-based automatic policy generation is implemented, then scalability and speed are improved, but system complexity increases
Solution Approach 1:
The system segments the complex policy generation process into distinct modular components: metadata extraction module, NLP intent analysis module, policy template matching module, and policy generation module. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable. This segmentation enables high-speed automatic policy generation while controlling complexity through modular architecture and clear separation of concerns.
Data Source
AI summary
One example method includes receiving user input concerning a data protection policy for an entity, where the user input is in the form of written input and/or audio input, when the user input comprises audio input, translating the audio input into text, determining an intention of the user with respect to data protection for the entity by translating the text to common terms by interpreting terms of the text that have the same meaning as being similar words and applying context to the words, identifying a data protection policy that best corresponds to the intention of the user concerning data protection for the entity, and applying the data protection policy to the entity.