Audit Trail Creation for Analytical Data Integrity
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Solution Overview
Problem
Current analysis information management systems face challenges in efficiently creating audit trails for data files from analytical instruments, as manual extraction of log information is cumbersome and prone to errors, making it difficult to guarantee data integrity, especially in large-scale systems with complex log data.
Innovation Solution
An analysis information management system that includes a client terminal with a file selector, audit trail information extractor, and audit trail creator, which automatically selects and organizes log information from a database using specific extraction keys such as instrument and user identifiers, and login identifiers to create a reliable audit trail in a time-series order, preventing human errors and ensuring data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual extraction of log information is performed to create audit trails, then flexibility and simplicity are maintained, but the process becomes cumbersome and error-prone, reducing reliability and productivity
Solution Approach 1:
The system performs preliminary actions by automatically extracting and organizing log information in advance, storing it in a structured format in the database. When an audit trail is needed, the pre-organized data is readily available, eliminating the need for manual extraction at the moment of need and ensuring both reliability and efficiency.
Solution Approach 2:
The system introduces an intermediary mechanism - an automated information extraction and organization module that acts as a mediator between the raw log data and the audit trail requirements. This intermediary automatically processes log information, extracts relevant data, and organizes it according to audit trail standards, thereby improving both reliability and productivity.
2Reliability
If all log information from multiple devices and operators is stored in the database, then complete audit coverage is achieved, but the complexity of extracting specific log information increases significantly
Solution Approach 1:
The system segments log information by storing it in a structured format with distinct fields for different types of data (device identifiers, user identifiers, timestamps, operation types, etc.). This segmentation allows the extraction process to query only specific segments relevant to the audit trail needs, rather than processing all log information uniformly, thereby reducing extraction complexity while maintaining completeness.
Solution Approach 2:
The system changes the parameters of log information storage by organizing data according to extraction keys such as device identifiers, user identifiers, and time periods. This parameter-based organization transforms the log data structure to enable efficient filtering and extraction, reducing the complexity of retrieving specific audit information from the comprehensive database.
3Productivity
If automated extraction using search functions is implemented, then productivity and reliability are improved, but the system requires more sophisticated database management capabilities
Solution Approach 1:
The system performs preliminary organization of log information during the data storage phase, structuring it with clear fields and indices based on anticipated extraction needs. This preliminary action reduces the complexity of subsequent automated extraction operations, as the data is already prepared for efficient querying and retrieval.
Solution Approach 2:
The system introduces an intermediary layer between the database and the audit trail creation process - an automated information extraction module that handles the complexity of database queries and data organization. This intermediary simplifies the overall system architecture by encapsulating database management complexity within a dedicated component, allowing other parts of the system to benefit from automated extraction without directly managing database complexity.
Data Source
AI summary
An analysis data collector stores data acquired with an analytical instrument in a file to which a data ID has been given, and registers the file in a database. A log information collector registers, in the database, log information which shows various operations on each analytical instrument or client terminal, state of the device or the like. After data files are selected, a command to create an audit trail is issued, whereupon an audit trail information extractor collects information corresponding to those data files. The audit trail information extractor extracts each piece of log information containing the data ID and pieces of log information which contain the device ID and user ID and were obtained within a time range from login to logout including the date and time of registration. An audit trail creator creates an audit trail by organizing the log information in time-series order, and registers it.