Use AI with evidence you can check

Suitable starting tasks include comparing two configuration snapshots, summarising a bounded log excerpt, grouping related events for review, and drafting a case update from approved facts. Define the question and the evidence boundary. Ask for references to supplied record identifiers and explicit separation of observation, inference, and missing information. An attractive narrative is not a substitute for those distinctions.

For incident investigation and event correlation, use the model’s suggestions to propose checks rather than certify causation. Similar timestamps or repeated names may be coincidental, and missing context can change the interpretation. Keep deterministic checks for details such as counts, identifiers, and time calculations. The analyst remains responsible for deciding whether the evidence supports the proposed conclusion.

Supply only the material required for the approved task. Prefer synthetic or appropriately sanitised examples when learning a workflow. Explain field meanings, time zones, known omissions, and what the model may not infer. Redaction should protect sensitive content without destroying the relationships needed for the analysis; replacing every identity with the same label can create false correlations.

Require output that can be reviewed systematically: a short claim, supporting record identifiers, uncertainty, and a proposed next check. Preserve the original artifacts outside the model response. For document creation, verify that a generated incident summary does not add affected systems, impact, or actions that were never established. A polished draft can hide unsupported detail more effectively than a rough note.

Evaluate the workflow using representative tasks with known evidence and review criteria. Check factual accuracy, omitted critical details, unsupported claims, and time spent correcting output. Include ambiguous and incomplete cases, not only easy examples. A tool that writes quickly but requires extensive correction may increase overall work or make important errors harder to spot.

If AI participates in automation or orchestration, constrain its role. It might suggest a search or draft an action request while a policy-controlled system checks permissions, scope, and approval. Log inputs and decisions according to the approved handling policy. Verify the actual result of any authorised action instead of relying on the model’s statement that it completed successfully.