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https://github.com/bayuncao/rdlp

A powerful, Rust-crafted tool for enterprise-grade data security. rdlp efficiently identifies and protects sensitive information, ensuring compliance and data integrity.
https://github.com/bayuncao/rdlp

dlp rust

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A powerful, Rust-crafted tool for enterprise-grade data security. rdlp efficiently identifies and protects sensitive information, ensuring compliance and data integrity.

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README 

        




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rdlp



A data security governance open-source tool and library developed in Rust.



English / [简体中文](./docs/README.zh-CN.md)



![Windows][Windows-image]

![MacOS][MacOS-image]

![Linux][Linux-image]

![Docker][Docker-image]



[Windows-image]: https://img.shields.io/badge/-Windows-yello?logo=windows

[MacOS-image]: https://img.shields.io/badge/-MacOS-black?logo=apple

[Linux-image]: https://img.shields.io/badge/-Linux-333?logo=ubuntu

[Docker-image]: https://img.shields.io/badge/-Docker-blue?logo=docker






## Background



To safeguard enterprise data security and privacy, our project (`rdlp`) encompasses a range of solutions dedicated to the identification and handling of sensitive data. These solutions encompass:



**Sensitive Data Identification Algorithms**: Our advanced algorithms are capable of effectively detecting sensitive information in text, images, and other data types. These algorithms are uniquely designed to adapt to various data formats and sources, ensuring comprehensive coverage and accuracy in identifying personal identity information, financial data, and other sensitive information.



**Data Desensitization Methods**: Once sensitive data is identified, `rdlp` offers a suite of data desensitization methods to secure data during storage and transmission. These methods, including data encryption, data masking, and data substitution, are customizable to meet specific requirements. `rdlp` continually updates these methods to counter evolving security threats, ensuring robust protection of sensitive data.



**Business Customization Options**: `rdlp` provides flexible customization options, allowing enterprises to tailor sensitive data handling methods and rules to their unique needs and privacy compliance standards. With user-friendly interfaces and APIs, `rdlp` enables easy integration and customization, aligning sensitive data processing with your business logic and compliance requirements.



**Mass Data Processing Capability**: Optimized for large-scale data, `rdlp` efficiently handles massive data streams and batches, maintaining data integrity and responsiveness. Our solution is designed to manage high-speed data flows and large data sets while ensuring efficient and secure handling of sensitive data.



The `rdlp` project also supports various privacy compliance standards, enabling the classification and labeling of original data, determination of sensitivity levels, and implementation of corresponding desensitization measures in accordance with these standards. This facilitates global compliance with multiple data protection regulations and provides sensitive responses to changes in these laws.



Our goal with `rdlp` is to provide enterprises with a comprehensive and customizable data security and privacy solution. We address the growing challenges of data privacy, ensuring the confidentiality and integrity of data in today's dynamic digital landscape.



## Features and Advantages

Comprehensive Data Protection: rdlp provides robust mechanisms for the identification and protection of sensitive data across various formats.

Customizable Solutions: Tailor the data protection strategies to suit specific organizational needs and compliance requirements.

Advanced Technology: Leverage cutting-edge algorithms for accurate data detection and secure desensitization techniques.



## Quick Start



## Installation Guide



## Usage Instructions



## Integrate



## Contribution

We welcome and encourage community contributions to rdlp. Here, you will find guidelines on how to submit issues, feature requests, or code contributions.



## License

rdlp is released under the [MIT license](LICENSE.md).



## Community

Join our community to participate in discussions, get support, and feedback.



## Contributors



## Changelog



## User Case