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 across AWS, Azure or GCP controls: IAM policy boundaries, KMS key hierarchies, VPC segmentation, CSPM tooling like Wiz or Prisma, and Terraform guardrails via OPA or SCPs.
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
Explains least-privilege IAM design, key rotation and network isolation at provider-specific detail, naming exact services, policy constructs and limitations.
02
Evaluation factor
Real incidents and findings
30% weight
Ask about breaches or audit findings they handled: exposed S3 buckets, leaked access keys, compromised CI runners, or SOC 2 and FedRAMP gaps they closed with dates and scope.
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 cloud incidents with detection source, blast radius, containment steps, and the architectural control added afterwards to prevent recurrence.
03
Evaluation factor
Risk judgement
20% weight
Test how they rank risk when engineering pushes back: public endpoints, shared service accounts, third party SaaS integrations. Look for threat modelling (STRIDE, MITRE ATT&CK cloud matrix) rather than checklist scoring.
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
Distinguishes theoretical findings from exploitable paths, justifies accepted risks with compensating controls and documents decisions for auditors and leadership.
04
Evaluation factor
Getting things fixed
15% weight
Check how they drove remediation across product teams: secure landing zones, paved-road modules, exception workflows, and evidence that misconfiguration counts or mean time to remediate actually dropped.
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 adoption metrics for reusable secure patterns and describes winning engineering buy-in without becoming a blocking approval gate.
Put this rubric to work
Score every candidate against the same standard
Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.
