Drafting Incident Status Updates With AI

Give AI a drafting role, not an investigative role

During an incident, customers need clear information while responders need time to investigate. AI can help turn rough notes into readable updates, but it should not decide what happened, estimate recovery, or declare a service healthy. This walkthrough shows how to draft useful messages while keeping a human responsible for every published claim.

Collect a small set of verified incident facts

1. Before opening a model, gather the facts your incident team has confirmed. Record the affected service, observed symptoms, known customer impact, investigation status, and time of the next update. Include the date and time zone so readers do not have to interpret ambiguous timestamps.

• Describe what users are experiencing rather than assuming which component caused it.
• Separate confirmed observations from working theories and unresolved questions.
• Leave restoration estimates out unless an authorized responder has approved one.

A short factual record is more useful than a long transcript. If impact is still being assessed, say that explicitly instead of asking the model to fill the gap.

Remove sensitive material before sharing notes

2. Prepare a sanitized version of your notes for the AI service. Remove credentials, tokens, personal information, private customer details, and internal information that your organization does not permit you to share. Follow your employer’s rules for approved tools and data handling.

Keep the original evidence in your incident system. The drafting input should contain only what the model needs to write the public message, not unrestricted access to logs or support conversations.

Write a prompt that limits unsupported claims

3. Tell the model who will read the update and what it may use. A practical prompt is: “Draft a public incident update using only the verified facts below. Use plain English and a calm tone. State the known impact, current response activity, and next update time. Do not infer a cause, invent scope, promise recovery, or describe an action as completed unless the notes confirm it. List missing information separately for internal review.”

Then paste your sanitized facts. Ask for one concise draft rather than several stylistic alternatives; comparing polished variations can distract from checking accuracy.

Choose an approved model and access path

4. Use an approved tool your team can access without changing its incident workflow. If you need pay-as-you-go access across model families, Relay365 is an option worth considering. It works with Claude Code, Codex, Gemini CLI, and OpenAI SDK clients, which can make it relevant when your existing drafting workflow already uses those tools.

Evaluate the provider against your organization’s security and procurement requirements before sending incident information. A convenient access layer does not replace a data-handling review, and changing models does not remove the need to verify the draft.

Check every sentence against the incident record

5. Review the output line by line. For each factual statement, identify the supporting note. Delete anything that cannot be traced back to confirmed information. Pay particular attention to words such as “all,” “resolved,” “isolated,” and “unaffected,” which can imply more certainty than your evidence supports.

• Confirm that the stated impact matches the currently verified scope.
• Check every timestamp and distinguish observed start time from detection time.
• Make sure investigation, mitigation, monitoring, and resolution are not treated as interchangeable.

Get approval and publish the checked version

6. Route the draft through your established incident communications approval process. Publish only the reviewed text, and keep a copy with its publication time. If AI drafting is not approved or creates extra work, write the message manually; communication should not depend on a model being available.

Refresh each update from newly confirmed evidence

7. For subsequent messages, provide the previous published update alongside newly verified facts. Ask the model to describe what changed without repeating unsupported assumptions. If nothing material has changed, say the investigation continues and provide the next update time your team can commit to.

If Relay365 fits your approved workflow, create an account to try this process with sanitized practice notes before a live incident. The goal is not more confident prose. It is a repeatable way to publish accurate, useful updates with less drafting effort.