Ensuring data privacy while leveraging AI-powered automation in Jira is essential for many organizations. The Private AI Mode in AI Issue Breakdown Assistant for Jira allows you to use your own AI model as an issue generation engine, ensuring that sensitive data remains within your controlled environment. This guide will walk you through the steps to configure Private AI Mode using a Groovy script for the integration.

Step 1: Understanding Private AI Mode

Private AI Mode enables you to integrate your own AI model, hosted within your data center, to handle issue generation. This setup ensures that all data processing happens internally, enhancing data privacy and security. The integration is achieved through a Groovy script that defines the communication protocol with the AI engine.

Step 2: Prerequisites

Before configuring Private AI Mode, ensure you have the following:

• Jira Data Center: Private AI Mode is compatible with Jira Data Center.
• Private AI Model: You need access to a Private AI model hosted within your data center. This could be a custom AI model or an on-premises deployment of an AI service.

Step 3: Installing AI Issue Breakdown Assistant for Jira

If you haven’t already, install the AI Issue Breakdown Assistant for Jira app from the Atlassian Marketplace. Follow these steps:

1. Navigate to the Atlassian Marketplace: Go to the Jira administration panel and select “Find new apps.”
2. Search for AI Issue Breakdown Assistant: Type “AI Issue Breakdown Assistant for Jira” in the search bar.
3. Install the App: Click “Get it now” and follow the on-screen instructions to complete the installation.

Step 4: Configuring Private AI Mode

Once the app is installed, follow these steps to configure Private AI Mode:

1. Access the Configuration Page: Navigate to the configuration page of AI Issue Breakdown Assistant for Jira within the Jira administration settings.
2. Select Private AI Mode: In the configuration options, under “Integration mode”, select “Private AI”
3. Configure the Groovy Script: Modify the provided Groovy script, under “Integration script”, to communicate with your Private AI model.

Step 5: Writing the Groovy Script

Here’s an example Groovy script to integrate with OpenAI GPT-4o, which you can modify for your AI model:

import com.atlassian.jira.util.json.*

def payload = new JSONObject() // Prepare body as per API specification
    .put("model", "gpt-4o")
    .put("messages", new JSONArray()
        .put(new JSONObject()
            .put("role", "user")
            .put("content", query)))
    .put("temperature", 1.0)
    .toString();

def post = new URL("<https://api.openai.com/v1/chat/completions>").openConnection() // Create a new request to specified URL
logger.debug("Preparing POST body: " + payload)

post.setConnectTimeout(60000) // Set a timeout to 60 seconds - make sure to always set timeouts
post.setReadTimeout(60000); // Set a timeout to 60 seconds - make sure to always set timeouts
post.setRequestMethod("POST") // Specify HTTP method
post.setDoOutput(true) // Specify our HTTP request has a body
post.setRequestProperty("Content-Type", "application/json") // Set HTTP header
post.setRequestProperty("Authorization", "Bearer REPLACE_ME") // Set HTTP header
post.getOutputStream().write(payload.getBytes("UTF-8")) // Set HTTP body

def postRC = post.getResponseCode() // Send POST request
def response = post.getInputStream().getText() // Read response body
logger.debug("Response code is: " + postRC + "; Response body is: " + response)

if (postRC.equals(200)) { // Verify request is successful
    def json = new JSONObject(response) // Read as JSON
    def result = json.getJSONArray("choices").getJSONObject(0).getJSONObject("message").get("content") // Traverse JSON tree
    return result // This script must always return the produced issues
}

throw new Exception("Unsuccessful request! HTTP response code is " + postRC + "; Response body is: " + response) // Throw error due to unsuccessful request

Step 6: Best Practices

When writing the script, please ensure you follow these best practices:

• Return Analytics as a String: The script must always return the generated issues as a String result.
• Error Handling: In case an error occurs, throw an Exception. Example: throw new Exception("An error has occurred...").
• Set Timeouts: Always ensure you put a timeout on all blocking operations, especially HTTP calls.
• Utilize Available Variables: Use query to access the issue content.

Step 7: Testing and Debugging

After configuring Private AI Mode, it’s crucial to test the setup:

1. Create a Test Issue: Create a new issue in Jira with a detailed description.
2. Generate Issues: Click the “Generate Issues” button on the issue view page to trigger the Groovy script.
3. Review AI Insights: Verify that the AI-generated issues are accurate and that the data processing is successful.

Troubleshooting Tips:

• Enable DEBUG Logs: To enable logging for AI Issue Breakdown Assistant, go to Jira Administration -> System -> Logging and Profiling and click on Configure under Default loggers. Add the following entry: com.deview_studios.atlassian.jira.dc.ai_issue_generator and set the logging level to DEBUG.
• Check Logs: Use logger.debug to log messages in the script and check the Jira logs for any errors.
• Verify Endpoint and Authentication: Ensure the AI model’s endpoint URL and authentication credentials are correct.
• Set Timeouts: Always set timeouts for HTTP requests to prevent hanging operations.

Configuring Private AI Mode in AI Issue Breakdown Assistant for Jira enhances data privacy and security by ensuring all AI processing occurs within your infrastructure. By following these steps and using the provided Groovy script, you can integrate your own AI model seamlessly. For more detailed documentation and support, visit the AI Issue Breakdown Assistant for Jira documentation page.