Voice cloning models can now produce real-time speech that matches tone, pacing, accent, and emotional modulation. This technology was first used for accessibility applications and media dubbing. It is now being used to impersonate candidates during interviews, enabling someone else to answer while sounding like the applicant.

The concern is not just impersonation. Voice cloning can mask hesitation, smooth out struggling phrasing, and create the appearance of confidence or fluency that does not match actual skill.

Security researchers in early 2025 demonstrated real-time voice cloning pipelines that require only 20 to 40 seconds of reference audio to generate convincing live speech.

How Deepfake Voices Appear in Interviews

Most deepfake voice usage follows these patterns:

1. Real candidate present, but speech masked to hide discomfort or language proficiency gaps.

2. Proxy candidate speaking, but voice cloned to resemble the original applicant.

3. Text-to-speech answer relay, where typed or whispered suggestions are instantly converted into speech.

These workflows are increasingly accessible through consumer-level tools.

Why Visual Identity Checks Are Not Enough

If a platform verifies only camera presence and not speech-behavior identity, a deepfake voice can pass without friction.

This is why interview intelligence tools now analyze:

• Response timing

• Latency in conversational turn-taking

• Phonetic micro-variation

• Stress-induced cadence changes

• Ability to re-explain concepts in new framing

Real reasoning exhibits variation. Deepfake relay systems often cannot adapt to unpredictable follow-ups.

Linguistic variability under conceptual constraint is a strong indicator of real cognitive authorship.

How Platforms Like Sherlock Respond

Sherlock AI does not attempt to “recognize” voices. Instead, it verifies thinking and authorship:

This approach remains fair to neurodivergent and multilingual candidates because it focuses on thought, not accent, tone, or confidence.

Audit Log Field Guide

What Hiring Leaders, Security Teams, and Legal Must Be Able to Trace

Interview platforms generate sensitive data. To maintain trust, legal defensibility, and internal accountability, the platform must provide audit logs that trace how data was accessed and how decisions were made.

Audit logs are not for monitoring employees. They exist to support compliance, post-hire investigations, and dispute resolution.

What an Audit Log Must Record

In multiple employment dispute cases from 2024 and 2025, lack of audit trails made hiring decisions legally indefensible.

Audit Log Format Requirements

Audit logs should contain:

• Timestamp

• Actor identity (mapped to SSO user)

• Object affected (interview, scorecard, export event)

• What changed

• Previous value (if applicable)

• IP or session metadata

This allows legal and compliance teams to reconstruct decision progression without guesswork.

How Sherlock Handles Audit Logging

Sherlock logs:

• Every access of interview summaries, notes, and recordings

• Scorecard submissions and revisions

• Export and share actions

• Role or permission updates

• Retention policy edits

Logs are retained in secure, hashed storage and can be exported to SIEM tools.

This supports:

• SOC2 Type II controls

• GDPR access accountability

• Internal compliance reviews

• External audits

Closing Insight

Both topics come down to one principle:

Hiring must measure real skill, expressed by the real candidate, recorded in a traceable and defensible way.

Deepfake voices test identity.
Audit logs protect trust.

Sherlock AI’s advantage is that it reinforces authentic thinking and documented fairness simultaneously.