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
Test depth on cloud control mapping: CIS Benchmarks, AWS Config rules, Azure Policy, IAM least privilege, encryption key custody, and evidence collection via Security Hub or Prowler.
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 control IDs, explains how policy-as-code enforces them, and distinguishes SOC 2 criteria from ISO 27001 Annex A requirements.
02
Evaluation factor
Real incidents and findings
30% weight
Probe audits they carried: SOC 2 Type II readiness, FedRAMP or PCI DSS scope, findings they remediated, and how they handled auditor evidence requests under deadline.
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 audit cycles with finding counts, sampling periods, and the specific misconfigurations or drift they closed before opinion issuance.
03
Evaluation factor
Risk judgement
20% weight
Assess how they triage cloud findings: public S3 buckets, over-permissive roles, unencrypted volumes, and which they accept as risk with documented compensating controls.
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
Ranks findings by blast radius and data classification, not scanner severity, and defends accepted risks with owner sign-off and expiry dates.
04
Evaluation factor
Getting things fixed
15% weight
Look for how they moved engineering teams: Terraform guardrails merged, Jira remediation SLAs, exception registers, and evidence that drift stayed closed after the audit.
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 remediation shipped as code or CI checks, with metrics on control coverage and repeat-finding rates falling quarter over quarter.
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.
