Interview scorecard template

Cybersecurity Threat Hunter interview scorecard

Evaluate Cybersecurity Threat Hunter candidates across 4 weighted areas: technical depth, real incidents and findings, risk judgement, and getting things fixed. Technical depth leads at 35%, so probe host and network internals, log sources, and query fluency, well beyond driving the SIEM console. Use the rubric to compare role-specific evidence consistently.

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security compliancecybersecuritydetection engineeringsocthreat hunting
TL;DR
For technical depth, look for evidence the candidate strong on host and network internals and fluent in query, hunting from a hypothesis rather than from alerts. For real incidents and findings, look for evidence the candidate names hunts that found real activity no alert caught, with the durable detections they built from it. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

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 host and network internals, log sources, and query fluency, well beyond driving the SIEM console.

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

Strong on host and network internals and fluent in query, hunting from a hypothesis rather than from alerts.

02
Evaluation factor

Real incidents and findings

30% weight

Look for hunts that found something an alert did not, and the detections that came out of it.

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

Names hunts that found real activity no alert caught, with the durable detections they built from it.

03
Evaluation factor

Risk judgement

20% weight

Test how they judge whether an anomaly is an intrusion or just an unusual administrator.

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

Separates genuine intrusion from benign anomaly efficiently, and can name a hunt they were wrong about.

04
Evaluation factor

Getting things fixed

15% weight

Assess how they hand a live finding to incident response without either panicking the org or underselling it.

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

Hands live findings to response with calibrated urgency and enough context to act immediately.

Evidence-led prompts

Interview questions for a Cybersecurity Threat Hunter

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Describe your experience with network traffic analysis: what do you actually look at, log by log?

  2. 02

    Describe your experience with endpoint detection and response solutions, including the queries you write yourself.

  3. 03

    What tools do you prefer for threat hunting, and why those over the alternatives?

  4. 04

    What methods do you use to identify potential security threats before an alert fires?

  5. 05

    Describe a time when you effectively mitigated a cybersecurity threat that no alert had caught.

See the complete Cybersecurity Threat Hunter question set
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