Detect: error or access concern
Preserve: question, versions, query, policy
Contain: sharing, cache, access
Repair: data, semantics, policy, model
Verify: regression, notice, prevention
Classify quality versus security
A bad definition, stale data, query failure, and misleading wording are different quality issues. Reading or exposing data outside authorization may be a security incident.
Apply the minimum necessary containment while facts are established, such as pausing an agent, revoking a link, or invalidating cache. Do not erase evidence.
Preserve reproducible evidence
Capture question, identity, project, time, conversation state, data and semantic versions, policy, query or tool calls, result summary, and sharing or export.
Logs may be sensitive. Restrict and encrypt them with retention rules; investigation does not justify indefinite storage of full business data.
Locate the responsible layer
Inspect source and freshness for data errors; metrics and relations for semantic errors; identity, row/column rules, cache, and output for access; models and tools for generation errors.
One incident can cross layers. Owners should agree on root cause and repair scope rather than patching a single example.
Replay related questions after repair
Build regression cases with paraphrases, roles, and boundary data. On fixed versions, verify expected results, denial, visibility, and audit.
If reports, notifications, or exports were affected, identify recipients and correct the record, not just new conversations.
Exercise response during selection
Inject a wrong definition, expired permission, cache contamination, and sensitive request during the POC. Test discovery, containment, replay, and evidence export.
AskTable.ai governance and analysis can participate, while log fields, retention, alerts, and security integration need explicit confirmation.
Public references
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