Assist before you automate

AI is effective at summarizing incidents, locating runbooks, and proposing queries. It becomes risky when it acts on incomplete context or hidden credentials.

Design for reviewability

Give every suggestion a source, a confidence signal, and a human approval point. Capture the outcome so the system can be audited and improved.

incident context → proposed diagnosis → operator review → controlled action

Good automation makes the next decision easier to inspect.

References

  • NIST AI Risk Management Framework

Keep AI actions reviewable

AI can reduce toil when it summarizes an incident, retrieves a runbook, explains a query, or drafts a change for an operator to review. It is much less trustworthy as an unsupervised production actor. Start with assistive use cases, expose the sources used for every recommendation, and require a human approval boundary before a change can affect a live system.

Treat the model, prompts, retrieved data, tools, and approvals as part of the operational system. Log them with appropriate privacy controls. Evaluate suggestions against real incident and change examples, including ambiguous cases where a confident but wrong answer would be costly.

Governance questions

  • What data can the assistant read and transmit?
  • Which actions require explicit operator approval?
  • How are prompt changes and tool permissions reviewed?
  • Can a responder reconstruct why a recommendation was made?

Further reading: NIST AI Risk Management Framework and NIST SSDF.