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Promoting to AI Team Edition

Switch from the AI Developer Edition to AI Team Edition.

In AI Developer Edition, data requiring encryption is processed through the Protegrity server. This is suitable for development and testing but lack the scalability and centralized management needed for production environments. To meet enterprise requirements such as robust logging, advanced security, and modular feature deployment, promote to AI Team Edition. The AI Team Edition leverages Protegrity Provisioned Cluster (PPC) which is a Kubernetes-based, cloud-native framework that enables secure, scalable operations and policy enforcement. Unlike traditional Protegrity setups that rely on Enterprise Security Appliance (ESA), AI Team Edition simplifies architecture by using PPC for collaborative, high-performance environments without the complexity of appliance clusters.

For more information about the AI Team Edition, refer to the AI Team Edition documentation.

Policy migration is not supported. Data protected in AI Developer Edition cannot be unprotected with the policy in AI Team Edition.

Key Advantages of AI Team Edition Over AI Developer Edition

  • Deployment and Architecture

    Team Edition is container-based and built on a microservices architecture, enabling fast deployment, simplified operations, and native integration with CI/CD pipelines. This aligns with modern DevOps practices and scales easily across environments.

    Developer Edition, by contrast, is primarily API-focused and intended for prototyping and development, not production. It uses a protector model and cannot go into production environments.

  • Feature Set

    AI Team Edition includes advanced capabilities such as:

    • Data Discovery and Classification for sensitive data (PII, PCI, PHI, IP).
    • Semantic Guardrails to enforce safe AI interactions.
    • On-demand Anonymization and Privacy-Safe Synthetic Data generation.
    • Integrated policy management and governance for compliance.

    Developer Edition offers the basic features of these for developers to experiment. It lacks enterprise-grade security and compliance features.

  • Use Case and Audience

    Team Edition is designed for small to mid-sized teams or departmental deployments that need production-ready data protection for AI and analytics workflows. It supports multiple protectors, enabling broader use cases beyond development.

    Developer Edition is strictly for initial development and prototyping; it cannot be scaled for production workloads.

  • Security and Compliance

    Team Edition embeds security directly into AI workflows, ensuring compliance without slowing innovation. It uses unique key material per customer, supports External Initialization Vectors (EIV), and enforces policy encryption over TLS.

    Developer Edition uses shared key material and mock protectors, making it unsuitable for production-grade security.

  • Cost and Scalability

    Team Edition offers a lower total cost of ownership for departmental deployments and can scale into Enterprise Edition later.

    Developer Edition is free for experimentation but has no upgrade path for assets created during development; moving to Team or Enterprise requires reconfiguration.

1 - Preparing for AI Team Edition

Understanding and preparing to move to AI Team Edition.

Moving from AI Developer Edition to AI Team Edition represents a significant step in scaling your AI development capabilities. AI Team Edition provides enhanced collaboration features, centralized management, and enterprise-grade controls designed for teams working together on AI-powered applications.

This guide walks you through the essential preparation steps to ensure a smooth transition from AI Developer Edition to AI Team Edition.

Understanding the process

Moving to the AI Team Edition involves updating the configuration of the AI Developer Edition artifacts to use the AI Team Edition features. The following image shows the features in the editions.

* - Available for purchase as an add-on. Can be installed as an individual product.

An overview of the process is provided here:

  1. Install the Protegrity Provisioned Cluster (PPC) and the required AI Team Edition features.
  2. Update the endpoints for Data Discovery, Semantic Guardrails, and Synthetic Data to point to the PPC.
  3. Install the AI Team Edition Application Protector Python modules and Application Protector Java libraries.

Note: Policy migration is not supported. Data protected in AI Developer Edition cannot be unprotected with the policy in AI Team Edition. Ensure that you unprotect the data before porting and reprotect it after the port to AI Team Edition is complete.

Feature version

Ensure that the final version you are porting to is the same or higher than the existing version. The version number of the features are provided here for reference.

Product nameAI Developer EditionAI Team Edition
Developer Edition API ServiceNot applicablePPC 1.0.0 with Protegrity Policy Manager
Data Discovery1.1.12.0.0
Semantic Guardrails1.1.01.1.1
Synthetic Data1.0.01.0.0
Application Protector Python1.0.01.0.0
Application Protector Java1.0.01.0.0

Installing the AI Team Edition

Install the AI Team Edition using the steps from the AI Team Edition documentation.

2 - Updating Python Modules

Steps for updating the Python modules.

Note: When the policy is set up on the AI Team Edition, ensure that the same data elements added for the AI Developer Edition are used. For more information about the data elements policy, refer to Policy Definition.
If you use different data elements while creating the policy, then modify the data elements used in the AI Developer Edition accordingly before running the modules.

  1. Install and set up the Protegrity Provisioned Cluster (PPC) and AI Team Edition using the steps from the PPC documentation and the respective feature documentation.

  2. Select the venv where protegrity-developer-python is installed.

  3. Install Application Protector Python using the steps from the Application Protector Python documentation.

    Note:
    When prompted for the ESA IP address, enter the hostname of the PPC. Similarly, when prompted for the ESA listening port number, enter 25400. This enables the protector to integrate with the PPC.
    After installation, the AP Python module of AI Developer Edition is replaced with the AP Python module of AI Team Edition.

  4. Run the samples.

3 - Updating Java Libraries

Steps for updating the Java libraries.

Note: When the policy is set up on the AI Team Edition, ensure that the same data elements added for the AI Developer Edition are used. For more information about the data elements policy, refer to Policy Definition.
If you use different data elements while creating the policy, then modify the data elements used in the AI Developer Edition accordingly before running the modules.

  1. Install and set up the Protegrity Provisioned Cluster (PPC) and AI Team Edition using the steps from the PPC documentation and the respective feature documentation.

  2. Install Application Protector Java libraries using the steps from the Application Protector Java documentation.

    Note: When prompted for the ESA IP address, enter the hostname of the PPC. Similarly, when prompted for the ESA listening port number, enter 25400. This enables the protector to integrate with the PPC.

  3. Include the ApplicationProtectorJava.jar in the classpath of your applications.

  4. Navigate to the location where the AI Developer Edition is cloned.

  5. Go to the protegrity-developer-edition/samples/java directory.

    a. Update the pom.xml and the application-protector-java dependency.

    ```
<dependency>
    <groupId>com.protegrity</groupId>
    <artifactId>application-protector-java</artifactId>
    <version>1.0.1</version>
    <scope>system</scope>
    <systemPath>/opt/protegrity/sdk/java/lib/ApplicationProtectorJava.jar</systemPath>
</dependency>
```

    Note: The AP Java libraries are expected to be in the default path /opt/protegrity/sdk/java/lib/. If the installation uses a different directory, update the environment or configuration so the system can locate the correct JAR files.

    b. Run the following command.

    ```
./mvnw clean package
```

    c. Update the sample shell script to include ApplicationProtectorJava.jar in the classpath.

    d. Run the samples.