What does AI proctoring do in an Intrvio interview?
AI proctoring collects integrity signals during the interview, such as tab switching, window focus loss, environment anomalies, and recording, then summarizes them into an integrity score for a human reviewer. It flags sessions that may need a closer look rather than automatically failing the candidate.
Integrity signals, not surveillance theater
Good proctoring measures meaningful integrity signals rather than collecting noise to look strict. Intrvio captures events like tab switches, repeated focus loss, and environment anomalies during the session, and records the interview so a reviewer has context for any flag.
These signals are summarized rather than acted on blindly. The point is to give a hiring team an honest picture of how the interview was taken, not to punish a candidate for a single tab change.
An integrity score for human review
Raw events are hard to act on, so Intrvio rolls them into an integrity summary that sits next to the transcript and scorecard. A clean session reads as low concern, while a pattern of risky signals is surfaced clearly for a reviewer.
Crucially, the integrity score is decision support, not a verdict. A human decides what a flagged session means in context, which keeps the process fair and explainable.
Built for fairness and disclosure
Candidates are told that the interview is proctored and what that involves, because hidden monitoring is neither fair nor compliant. Transparent disclosure keeps Intrvio aligned with KVKK and GDPR expectations and EU AI Act deployer duties.
Proctoring is tuned to avoid over-flagging normal behavior. The goal is to catch coordinated or clearly anomalous patterns, not to penalize nerves, accessibility needs, or a noisy room.
Where proctoring helps most
Proctoring earns its place in high-volume remote screening, where teams cannot personally supervise every interview but still need confidence in the results. It pairs naturally with identity verification so you know both who took the interview and how.
For sensitive or borderline cases, the recording and signal trail let a human reviewer make a calibrated call instead of relying on a gut reaction.