Using generative AI to explain failed integration messages, safely

The problem

Support teams spend most of their incident time reading logs to answer one question: what went wrong and what should we do? Generative AI is very good at this explanation step, but sending raw payloads and logs to a model is a data protection risk.

The architecture

• Trigger: a failed message raises an alert, or a scheduled flow collects new errors from the message processing log.
• Collect: gather the error text, the failing step, the adapter, the iFlow name and recent similar errors.
• Mask: remove payload content, names, IDs, URLs and credentials before anything reaches the model.
• Ask: send a fixed prompt template through SAP’s Generative AI Hub, asking for a likely cause, a suggested fix and a confidence level.
• Deliver: attach the explanation to the alert or ticket, clearly marked as AI-suggested.
• Review: a consultant confirms or corrects it, and confirmed answers become your growing knowledge base.

A prompt template that works

You are an SAP Cloud Integration support specialist.
Error text: {masked_error}
Failing step: {step_type}   Adapter: {adapter}
Explain the most likely cause in two sentences, list up to three
checks to confirm it, and state your confidence as low, medium or high.
Do not guess values that are not in the input.

Guardrails

• Never send message payloads or personal data, only masked error metadata.
• Keep the prompt template under version control and review changes.
• Log every request and response for audit.
• Never let the AI change configurations or resend messages on its own.

Result

Support teams start every incident with a clear hypothesis instead of a wall of logs, while sensitive data stays inside your landscape and a person stays accountable for every fix.