Simply configure Go to achieve sensitive data desensitization, and you can customize data desensitization rules (complete implementation source code attached).
Introduction
In order to ensure the data security and privacy of enterprises, godlp provides a series of identification and disposal solutions for sensitive data, including sensitive data identification algorithms, data desensitization processing methods, business-customized configuration options and massive data processing capabilities. godlp can apply a variety of privacy compliance standards to grade and mark raw data, determine the sensitivity level and implement corresponding desensitization processing.
Key capabilities
godlp can widely support structured (Json data, KV data, golang map) and unstructured data (multilingual strings).
Automatic discovery of sensitive data: DLP has built-in multiple sensitive data identification rules, which can identify sensitive types of original data to ensure that sensitive information can be properly processed.
Sensitive data desensitization processing: DLP supports a variety of desensitization algorithms, and businesses can perform different desensitization processing on sensitive data according to needs.
Business custom configuration options: In addition to the default sensitive information identification and processing rules, the business can configure custom YAML rules according to actual conditions, and DLP can complete corresponding data processing tasks based on the incoming configuration options.
DLP basically uses
godlp SDK to mainly provide the following APIs for sensitive information identification and desensitization.
In addition to the methods provided above, dlp provides default configuration rules. The configuration file is in yaml format and is divided into three parts: Global, MaskRules, and Rules. Among them:
Global contains some configuration items that affect DLP globally, such as API version, disabled rule ID, and whether to enable back-end service auxiliary judgment.
MaskRules contains desensitization operations