Automated DSAR Processing via AI Trust Center
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Solution Overview
Problem
Conventional DSAR systems face challenges in ensuring regulatory compliance, operational efficiency, and user experience due to complexity, cost, and resource constraints, particularly for small and medium-sized enterprises, with inaccuracies and incomplete data leading to compliance issues and potential data breaches.
Innovation Solution
An AI-driven and NLP-integrated system for automated DSAR processing, which includes a Trust Center and compliance platform for seamless data management, automated redaction, and anonymization, along with a user-friendly interface and metrics dashboard for performance tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated DSAR processing systems are implemented, then operational efficiency is improved, but device complexity and implementation cost increase
Solution Approach 1:
The DSAR processing system is divided into distinct functional modules: a request reception module that receives DSARs, an NLP interpretation module that processes natural language requests, an automated workflow engine that executes processing steps, and a fulfillment module that delivers results. This segmentation allows each module to be optimized independently while maintaining overall system efficiency.
Solution Approach 2:
An automated workflow engine acts as an intermediary between the NLP interpretation module and the fulfillment module. It receives interpreted request parameters, dynamically generates processing workflows based on those parameters, and coordinates the execution of multiple processing steps, thereby managing system complexity through centralized orchestration.
2Productivity
If NLP and AI technologies are integrated for automated processing, then productivity increases, but device complexity increases
Solution Approach 1:
The NLP engine automatically interprets DSAR requests without requiring manual analysis, extracting key parameters such as data types, time ranges, and format preferences directly from the natural language input. This self-service capability eliminates the need for manual request analysis and significantly increases processing throughput.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the interpreted DSAR request. The automated workflow engine generates different processing workflows depending on the data types requested, the jurisdiction applicable, and the preferred delivery format, allowing the system to adapt to varying request complexities without manual intervention.
3Device complexity
If manual DSAR handling is used, then system complexity is reduced, but loss of time and productivity decrease
Solution Approach 1:
The system performs preliminary actions by pre-defining processing workflows for different DSAR scenarios. When a request is received, the automated workflow engine selects and executes the appropriate pre-configured workflow, eliminating the need to design processing steps from scratch for each request and significantly reducing processing time.
Solution Approach 2:
Manual mechanical processing steps are replaced with automated electronic workflows. The system uses software-based workflow engines, database queries, and automated document generation to replace manual data retrieval, analysis, and response preparation, thereby reducing processing time while maintaining manageable complexity through automation.
4Reliability
If comprehensive data retrieval is performed to ensure accuracy, then reliability improves, but loss of time increases
Solution Approach 1:
The system performs preliminary data retrieval by querying multiple data sources simultaneously based on the interpreted request parameters. Rather than sequentially accessing each data source, the workflow engine parallelizes data retrieval operations, ensuring comprehensive data collection while minimizing processing time.
Solution Approach 2:
The automated workflow engine maintains continuous processing by orchestrating data retrieval, validation, redaction, and response generation in an uninterrupted sequence. This eliminates idle time between processing steps and ensures that comprehensive data accuracy checks are performed without significant time delays.
Data Source
AI summary
This present invention provides a method and system configured for handling DSARs within regulated environments, emphasizing efficiency, compliance, and transparency. By incorporating AI, NLP, and automated data management technologies, the invention provides a system and method for significantly reducing manual effort and improves compliance outcomes.