AI & Technical question

Enterprise customers want to use Claude for vulnerability discovery and remediation today, but performance varies by finding type. How would you set the bar for what can ship now versus what must remain a research target? Be explicit about how you'd evaluate false positives, false negatives, human-in-the-loop requirements, and customer risk tolerance.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Ability to set a nuanced, finding type specific launch bar for an AI capability with uneven reliability, rather than one blanket decision.

Segment vulnerability finding types by how well the model performs today, for example well understood pattern classes like SQL injection versus complex logic flaws requiring deep context.
For each segment, define the false positive cost, wasted security engineer time, and false negative cost, a real vulnerability missed, since these differ by finding type and customer risk tolerance.
Set the human in the loop requirement per segment: high confidence, well understood finding types can suggest fixes directly for review, while low confidence categories should only flag for human investigation, not auto remediate.
Ship the well understood, high precision finding types now, keeping the weaker categories as a labeled research or beta target with clear customer expectation setting.
Build a feedback loop from customer usage on shipped categories to prioritize which research target categories to promote next.
Communicate the split explicitly to customers so they understand the confidence based boundary between shipped and beta capability.

Segments the launch decision by finding type rather than making one blanket good or bad call on the whole capability.
Names concrete asymmetric costs, a missed vulnerability being worse than a false positive in security contexts, and reflects that in the human review bar.
Proposes different automation levels, suggest versus flag only, mapped to confidence per segment.
Builds an explicit path for research target categories to graduate to shipped status based on evidence.

Making a single launch decision for the whole capability instead of segmenting by finding type.
Underestimating the cost of a false negative in a security context relative to a false positive.

How would you communicate to a customer which finding types are trustworthy today?
What evidence would move a research target category into general availability?