Product design question

You are designing a Codex-based workflow that helps analysts create, test, and deploy detection content inside their existing security stack. What product requirements would you define so that analysts can trust the output enough to use it in production? Be specific about inputs, review and approval steps, evidence shown to the analyst, failure handling, and how the workflow fits into real detection engineering habits.

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What this question tests

Product design discipline for an AI workflow whose output must be trusted enough for production security use, not just impressive in a demo.

Define inputs precisely: the workflow should ingest an analyst's plain-language detection intent plus context from their existing stack, like log schema and prior detection rules, not a blank prompt.
Require a mandatory human review and approval step before any detection content deploys to production, with the diff between proposed and existing rules shown clearly.
Surface evidence alongside the output: show the specific log samples or attack patterns the detection logic was built to catch, so the analyst can judge coverage, not just trust a black box.
Design failure handling: if the agent cannot generate a rule with sufficient confidence, it should say so explicitly and suggest what additional context would help, rather than producing a low-quality rule silently.
Fit the workflow into real habits: integrate directly into the analyst's existing detection engineering tool and version control process rather than a separate standalone interface they must context-switch to.

Grounds inputs in the analyst's real context, existing rules and log schema, rather than a generic prompt box disconnected from their actual stack.
Makes human review and approval a hard requirement before production deployment, directly addressing the trust bar the question sets.
Shows supporting evidence, like the log samples the rule was built against, so trust is earned through visible reasoning, not blind faith in the output.
Designs explicit low-confidence failure handling, which prevents the common failure mode of an agent confidently producing a subtly wrong detection rule.

Proposing a standalone chat interface disconnected from the analyst's existing detection engineering tools and version control.
Skipping explicit failure handling, letting the agent silently produce a low-confidence rule without flagging uncertainty.

How would you test whether a generated detection rule actually catches the intended attack pattern before deployment?
What would you do if analysts started rubber-stamping approvals without real review?