The support lead asks: what needs attention right now?
The dangerous implementation gives an AI assistant raw ticket tables.
Those tables contain names, emails, internal notes, copied logs, attachments, security details, and sometimes secrets that should never have been pasted there.
The useful workflow returns less.
Start with a governed summary:
- overdue count
- unassigned count
- reopened count
- age bands
- approved urgency categories
- data freshness
Then make drill-down progressive:
- aggregate summary
- bounded worklist with ticket IDs and controlled fields
- separately authorized, redacted detail for one case
Each expansion is a new policy decision.
Keep tenant and queue scope in authenticated context. Keep metric definitions in approved views or a semantic layer. Keep raw notes, contact details, attachments, and unrestricted free text outside the first result.
And separate analysis from action.
A tool that identifies five tickets needing owners should not automatically gain authority to assign them, send replies, or close cases. Those are separate operations with validation, idempotency, and approval where needed.
The goal is not to expose the inbox.
It is to expose the smallest decision signal that helps the team act.
Full workflow: ChatGPT database query for support triage
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