Protegrity AI Developer Edition Features

The following features are available with Protegrity AI Developer Edition:

• Data Discovery: Identify sensitive data across your organization using AI-powered scanning and classification.
• Semantic Guardrails: Implement AI-driven policies to protect sensitive data while allowing for flexible access controls.
• Synthetic Data: Create realistic synthetic data for testing and development without exposing real sensitive information.
• Anonymization: Use AI techniques to anonymize or mask sensitive data while preserving its utility for analysis and development.
• Data Protection: Implement encryption and tokenization to secure sensitive data.

In AI Developer Edition, a sample file is used by the sample application, which is processed by the Data Discovery container. The containers detect sensitive data. A Python module then redacts, masks, or protects and unprotects the data. The sanitized file is saved to a configured location. For more information about the sample application, refer to Sample applications.

Use the steps provided to run the application end-to-end. If required, run the APIs and functions provided for performing specific tasks. For more information about the APIs, refer to the respective Feature APIs.

The sample applications are grouped below based on whether they require a free account registration.

No registration required

The following sample applications can be run by deploying the respective containers without any registration:

• Running Data Discovery
• Running Semantic Guardrails
• Generating synthetic data
• Anonymizing data

Free registration required

Data Protection sample applications require a free account registration. The following sample applications can be run by deploying the respective containers after registering for a free account:

• Data Protection

Sample Applications

Protegrity AI Developer Edition provides Python and Java applications that showcase the features of Protegrity products.

sample-app-find

The sample-app-find is a Python or Java application that processes and identifies sensitive data.

It can be customized to do the following functions:

• Specify a file name and output location for the source data. Only raw file formats are supported for Data Discovery. Multipart formats are not supported; only binary files are accepted.

sample-app-find-and-redact

The sample-app-find-and-redact is a Python or Java application that processes the identified data and redacts or masks the information.

It can be customized to do the following functions:

• Specify the items that must be identified.
• Specify the operation to be performed on the data, which is redact or mask.
• Specify a file name and output location for the source data. Only raw file formats are supported for Data Discovery. Multipart formats are not supported; only binary files are accepted.
• Specify a file name and output location for the transformed data.

sample-guardrail-python

The sample-guardrail-python is a Python application that submits a request to Semantic Guardrails for analysis.

It can be customized to do the following functions:

• Specify the data that must be processed.
• Specify the operation that must be performed, that is, semantic processor for messages and pii processor for AI.

sample-app-find-and-protect

The sample-app-find-and-protect is a Python or Java application that processes the identified data and protects the information. Calls are made to the AI Developer Edition API Service for performing tokenization.

It can be customized to do the following functions:

• Specify the items that must be identified.
• Specify a file name and output location for the source data. Only raw file formats are supported for Data Discovery. Multipart formats are not supported; only binary files are accepted.
• Specify a file name and output location for the transformed data.

sample-app-find-and-unprotect

The sample-app-find-and-unprotect is a Python or Java application that unprotects the information protected by the sample-app-find-and-protect module. Calls are made to the AI Developer Edition API Service for performing detokenization.

It can be customized to do the following functions:

• Specify a file name and output location for the source data. Only data protected by the sample-app-find-and-protect module can be unprotected.
• Specify a file name and output location for the transformed data.

sample-app-protection

The sample-app-protection is a Python or Java application that protects and unprotects data. Calls are made to the AI Developer Edition API Service for performing tokenization. The Data Discovery and Semantic Guardrails containers are not required to be running for the sample-app-protection module to work.

It can be customized to do the following functions:

• Specify the items that must be protected, data element name, and user.
• Specify the operation that must be performed, protect and unprotect.

sample-classification--text

The sample-classification--text is a Python, Java, or Jupyter application that classifies the text data. Calls are made to the Data Discovery API Service for performing classification.

It can be customized to do the following functions:

• Specify the data that must be classified.
• Specify the type of classification, which is either pii or semantic.

sample-classification--tabular

The sample-classification--tabular is a Python, Java, or Jupyter application that classifies the tabular data. Calls are made to the Data Discovery API Service for performing classification.

It can be customized to do the following functions:

• Specify the data that must be classified.
• Specify the type of classification, which is either pii or semantic.

sample-redaction-

The sample-redaction- is a Python, Java, or Jupyter application that redacts the data. Calls are made to the Data Discovery API Service for performing classification and redaction.

It can be customized to do the following functions:

• Specify the data that must be redacted.
• Specify the type of redaction, which is either pii or semantic.