Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical depth
35% weight
Probe depth on presentation and injection attack detection: ISO/IEC 30107-3 PAD levels, passive versus active liveness, virtual camera and emulator detection, C2PA Content Credentials, watermarking schemes.
Evidence to listen for
Command of the specific attack surface, tooling, and controls the role covers
Understands how the underlying system works, not just how the tool reports on it
Can explain an attack or control chain end to end
Distinguishes what they found themselves from what a scanner flagged
Five-point scoring guide
1
Poor
Tool operator only; no understanding of the systems underneath.
2
Needs Improvement
Runs tooling but cannot explain findings or how the attack works.
3
Satisfactory
Solid working knowledge; depth thins outside familiar tooling.
4
Very Good
Strong command of the domain; explains attack and control chains clearly.
5
Excellent
Names specific detection stacks, explains APCER/BPCER trade-offs at chosen thresholds, and distinguishes presentation attacks from injection at the SDK layer.
02
Evaluation factor
Real incidents and findings
30% weight
Ask for real synthetic-media incidents they handled: voice-cloned executive payment fraud, morphed passport photos at onboarding, injected video in KYC flows, or iBeta and NIST FATE evaluation results.
Evidence to listen for
Brings specific incidents, findings, or audits they personally worked
States their own role rather than the team's
Describes what was actually at risk and what changed afterwards
Can talk about a finding that turned out to be wrong
Five-point scoring guide
1
Poor
No hands-on work; knowledge is entirely certification or coursework.
2
Needs Improvement
Limited exposure; cannot describe their contribution to an incident.
3
Satisfactory
Real casework with adequate detail; ownership sometimes vague.
4
Very Good
Specific incidents with clear personal scope and what changed after.
5
Excellent
Recounts named incidents with attack tooling, bypass method, detection gap found, and the architecture change that closed it.
03
Evaluation factor
Risk judgement
20% weight
Test how they rank threats across channels (call centre, video KYC, contact-centre IVR, internal approvals) against false-reject cost, accessibility impact, and regulatory exposure under eIDAS or AML rules.
Evidence to listen for
Prioritises by actual exploitability and business impact, not raw severity scores
Can argue for accepting a risk as well as fixing it
Knows the difference between a finding and a problem
Does not cry wolf or wave things through
Five-point scoring guide
1
Poor
Treats every finding as critical, or waves real risk through.
2
Needs Improvement
Follows severity scores mechanically; no business context.
3
Satisfactory
Reasonable prioritisation; less confident arguing for risk acceptance.
4
Very Good
Prioritises by exploitability and impact; can justify accepting a risk.
5
Excellent
Prioritises by attacker economics and fraud loss data, accepts residual risk explicitly, and refuses detection theatre that harms legitimate users.
04
Evaluation factor
Getting things fixed
15% weight
Look for evidence they moved vendors, engineers, and fraud teams to deploy controls: signed provenance pipelines, step-up verification playbooks, model refresh cadence, retraining against new generator families.
Evidence to listen for
Writes findings engineers can act on rather than a wall of output
Has persuaded a team to fix something they did not want to fix
Explains risk to executives in business terms
Works with the org rather than policing it
Five-point scoring guide
1
Poor
Adversarial with engineering; findings never get fixed.
2
Needs Improvement
Reports are unactionable; no influence beyond raising tickets.
3
Satisfactory
Adequate reporting; relies on mandate rather than persuasion.
4
Very Good
Actionable findings and a real record of getting fixes shipped.
5
Excellent
Shows shipped controls with adoption dates, monitoring of detector drift, and documented escalation paths owned by named business teams.
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