The intent of this phase is to establish a robust Data Validation and Quality Control (QC) framework for assessing and ensuring the integrity and accuracy of the research data on the OWASP Top 10 for LLM AI Applications. This step is crucial for maintaining the reliability of our findings and ensuring that the data reflects real-world vulnerabilities accurately.
- Define Key Performance Indicators (KPIs): Establish both qualitative and quantitative measures that will guide the validation process.
- Develop Validation Tools: Create scripts and utilize automated tools to systematically verify the data against our KPIs.
- Ensure Data Quality: Implement a QC process to identify and rectify any inconsistencies, inaccuracies, or biases in the dataset.
The provided Python scripts serve as a baseline for conducting data validation and quality control. These scripts are designed to be adaptable to various datasets and environments, depending on the specific needs of the research. Below is a brief overview of what to expect:
- Data Consistency Checks: Scripts to verify that the data is consistent across different sources and timeframes.
- Accuracy Tests: Tools to compare sampled data against trusted benchmarks or manual checks to assess accuracy.
- Completeness Checks: Automated checks to ensure that the dataset is complete and all expected data points are present.
- Automated Anomaly Detection: Scripts to identify outliers or anomalies in the data that may indicate errors or inconsistencies.
- Bias Detection: Tools to assess the dataset for any potential biases that could affect the research outcomes.
It is expected that the provided scripts will need to be adapted for each specific research environment and dataset. This adaptation may involve:
- Parameter Tuning: Adjusting thresholds, weights, or other parameters within the scripts to better fit the specific data characteristics.
- Custom Checks: Adding or modifying checks and validations to address unique aspects of the data or research objectives.
- Integration with Other Tools: Modifying the scripts to work seamlessly with other tools or platforms used in the research process.
To begin using the validation and QC scripts, please ensure you have the following prerequisites installed:
- Python 3.8 or higher
- Necessary Python libraries as listed in
each md file
Follow the setup instructions in SETUP.md to configure your environment and adapt the scripts to your dataset. For detailed documentation on each script and how to customize it, refer to the md files.
Your contributions are welcome! If you have suggestions for improving the validation and QC methodology, please open an issue or submit a pull request with your proposed changes. For more information on contributing, please see CONTRIBUTING.md.
Thank you for your interest in improving the security and reliability of LLM AI applications.