Enhancing DSPM Efficiency with Customized QA Automation
DSPM
Engagement Highlights - QA
- Developed a comprehensive automation framework for DSPM, covering data generation, scanning, and validation.
- Created structured and unstructured data stores using Azure SDK, populated with test data.
- Integrated DSPM product APIs to scan data stores for sensitive information.
- Implemented scripts to validate scanned data against test data, ensuring accurate sensitive data identification.
Introduction
The company is a leading provider of cloud data security solutions across hybrid cloud environments.
Challenges & Goals
Neova team was tasked to perform QA activities for DSPM domain product with below goals –
- Write data store generator scripts using Azure cloud SDKs.
- Run the automated APIs to scan the data stores for sensitive data.
- Write the validator scripts to validate the input sensitive test data with the scanned data and give the results.
Solutions
After getting a clear understanding of the requirements of the end user, Neova developed a comprehensive automation framework, accomplishing the following key tasks:
- Generated Structured and Unstructured Data Stores in Azure
Utilizing the Azure SDK, Neova created various data stores within the Azure environment. This included both structured data (e.g., SQL databases) and unstructured data (e.g., blob storage).
The data stores were populated with test data that potentially contained sensitive information, providing a realistic testing ground for the engagement.
2. Triggered APIs to Scan the Data Stores
Neova integrated with DSPM product’s APIs to initiate scans of the created data stores.
The framework was designed to automate the process of triggering these scans, ensuring that each data store was systematically evaluated for sensitive data.
3. Developed Validator Scripts
Validator scripts were written to compare the actual data in the data stores against the results obtained from the scan.
These scripts checked for discrepancies and validated whether the sensitive information was accurately identified by the DSPM product.
The validation process included detailed checks for various types of sensitive information, ensuring comprehensive coverage and accuracy.
Business Impact
- Efficiency: The automation framework significantly reduced the manual effort required to validate sensitive data, leading to faster and more reliable results.
- Accuracy: The detailed validator scripts ensured that sensitive data was accurately identified, enhancing the credibility of the DSPM product.
- Scalability: The modular nature of the framework allows for easy expansion to accommodate additional data types and validation rules in the future.
- Bug Identification and Reporting: During the validation process, Neova team identified and reported several bugs related to the product scanner. These included instances where the scanner failed to identify sensitive data and cases where it mistakenly identified non-sensitive data as sensitive. Addressing these issues helped improve the overall accuracy and reliability of the DSPM product.
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