Content Security Classification via AI Drafting
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Solution Overview
Problem
Existing records and content management systems face challenges in efficiently and accurately applying security labels and user permissions, leading to laborious manual processes and potential over-security of content items.
Innovation Solution
The implementation of a computer-implemented system that provides restriction groups and associated restriction marks within a content management system, allowing for streamlined selection and assignment of security labels to content items through a graphical user interface, thereby updating metadata for access control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for applying security labels and user permissions, then flexibility and control are maintained, but the process becomes laborious and time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically generating draft security classifications using AI/ML models before user review. This preliminary classification reduces the time and effort required for manual security labeling while maintaining control, as users only need to review and confirm rather than create classifications from scratch.
Solution Approach 2:
An AI/ML-based intermediary system is introduced between the user and the security classification process. This intermediary automatically analyzes content items and proposes security labels, acting as a mediator that handles the laborious analysis work while allowing human users to maintain final control over the classification decisions.
2Reliability
If comprehensive security protocols are enforced to ensure proper classification, then security is improved, but user productivity decreases due to additional workload
Solution Approach 1:
The system enables self-service by allowing the AI/ML model to automatically perform the security classification analysis and draft the labeling. Users simply need to review and confirm the automated suggestions, transforming a complex manual task into a simple verification process that maintains security accuracy while preserving productivity.
Solution Approach 2:
The system performs preliminary security analysis and draft classification before user review, so that when users do engage with the process, the heavy lifting of analysis is already complete. This ensures comprehensive security protocols are enforced through automated analysis while minimizing the additional workload on users to merely confirm the pre-analyzed results.
3Reliability
If multiple security parameters are applied to content items, then security coverage is improved, but the complexity of managing these parameters increases
Solution Approach 1:
The system merges multiple security parameters and classification criteria into a unified automated analysis process. The AI/ML model simultaneously evaluates multiple security dimensions (user permissions, content sensitivity, distribution restrictions) and integrates them into a cohesive security classification, reducing the complexity of managing individual parameters while maintaining comprehensive security coverage.
Solution Approach 2:
The AI/ML-based security classification system performs multiple functions simultaneously: analyzing content, determining appropriate security labels, assessing user permissions, and generating draft classifications. This universal system handles what would otherwise require multiple separate manual processes, reducing overall complexity while maintaining comprehensive security parameter application.
4Measurement precision
If users are required to carefully select appropriate security labels, then classification accuracy is improved, but the process becomes more time-consuming
Solution Approach 1:
The system performs preliminary analysis and generates draft security label suggestions based on automated content evaluation. Users then only need to review these pre-analyzed suggestions and make minor adjustments if needed, rather than conducting the entire analysis themselves. This maintains classification accuracy through careful selection while dramatically reducing the time required for label selection.
Solution Approach 2:
The system provides feedback to users in the form of AI-generated draft classifications with explanations of the reasoning. Users can review this feedback, confirm accurate suggestions, or provide corrections. This feedback mechanism maintains high classification accuracy by leveraging both automated analysis and human judgment, while reducing time by eliminating the need for users to start the analysis from scratch.
Data Source
AI summary
Computer-implemented systems, method and products configured for providing one or more restriction groups in a content management system are provided. One or more restriction marks may be associated with the one or more restriction groups. At least a first restriction mark may be associated with a first restriction group. The first restriction mark may be assigned to a first content item stored in the content management system, in response to determining that the first content is associated with the first restriction group, the first content item being associated with metadata indicating user access permissions according to the first restriction mark and a security classification. The metadata associated with the first content item may be updated based on the assignment of the first restriction mark to the first content item to allow or limit user access to the first content item.