How to Detect AI Cheating in Interviews

If you are interviewing and worried a candidate is using AI, the honest starting point is that you cannot scan their machine. This section covers what is actually observable versus what is myth, so you watch the right signals.

An AI assistant running locally on the candidate's own device produces no network signature you can read from your side, and there is no reliable service that flags it. What you can observe is the conversation itself. SubcueAI is designed to be discreet, and it also states plainly that screen-share, recording, and proctored or company-managed devices are out of scope, the same honesty any interviewer should apply to detection claims. The detectability topic page covers this in more depth.

Real detection is reading the conversation, not the network. A few signals correlate with reading or relaying an answer:

• A consistent pause before fluent, polished answers, then stumbling on follow-ups.
• Eyes tracking across a screen rather than looking at the camera or thinking.
• Answers phrased like written prose, with detail that does not survive a why or how follow-up.
• A mismatch between the resume's depth and the spoken depth.

None of these is proof on its own; together, across several questions, they form a pattern.

The most reliable detector is a good follow-up question. A candidate relaying a generated answer can deliver the first response cleanly and then fail to go deeper, because the understanding behind it is not theirs.

An interviewer probing a backend candidate might accept a clean answer on database indexing, then ask why a specific index hurt write throughput in their own project; the gap between the polished surface and the shallow specifics is the tell. This is also why SubcueAI frames its help as preparation and structure rather than a guarantee: under real follow-up, only actual understanding holds. How SubcueAI handles data and what stays on the device is on the security page.

Some formats remove the guesswork. Screen-share shows exactly what is on the candidate's display. Recording lets a reviewer re-watch for the signs above. Proctoring software and company-managed devices can block or flag local apps directly. In those settings, no candidate-side tool is safe, and detection is not really the question.

If you want candidates to compete on understanding, structured follow-ups plus one of these formats does more than any detector claim. SubcueAI itself is honest that these settings are out of scope, the same line it takes across the SubcueAI site and answer library.