Datadog

What is Datadog?

DataFog is an open-source privacy platform designed to detect and anonymize Personally Identifiable Information (PII) in various types of files, including text, image, and audio files. Users can utilize DataFog to scan data for sensitive information, redact or replace it with custom synthetic data. This capability enables the creation of PII-free datasets that can be safely shared with trusted third parties.

Who created Datadog?

Datafog was created to provide an open-source privacy platform for detecting and anonymizing Personally Identifiable Information (PII) in various types of files. The platform enables users to scan data for sensitive information and either redact it or replace it with synthetic data, allowing the creation of PII-free datasets for sharing with trusted third parties.

What is Datadog used for?

• Scan data for sensitive information
• Redact sensitive data
• Anonymize PII in text, image, and audio files
• Swap-in custom synthetic data
• Create PII-free datasets
• Share datasets with trusted third parties
• Redact sensitive information

Who is Datadog for?

• Data scientists
• Data Analysts
• Privacy Officers
• Compliance Officers

How to use Datadog?

To use DataFog, follow these steps:

1. Start by pulling and running the DataFog API locally using the command:

docker run -p 8000:8000 datafog/datafog-api:latest

2. Access the DataFog API at http://localhost:8000/docs to begin interacting with the platform.

3. DataFog is designed to scan data for sensitive information such as Personally Identifiable Information (PII) in text, image, and audio files.

4. Users can leverage DataFog to anonymize PII by redacting or substituting it with custom synthetic data.

5. Utilize DataFog to create datasets free of PII, making them safe for sharing with trusted third parties.

By following these steps, you can effectively utilize DataFog to detect and handle sensitive information in your data files.

Please note that additional details and support can be found on the DataFog Docker Hub, GitHub repository, and through the provided contact links.