security complianceai governanceeu ai actmodel risknist ai rmf
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
Check command of NIST AI RMF, ISO/IEC 42001 and EU AI Act obligations, plus fairness metrics (demographic parity, equalised odds), model cards and LLM red-teaming methods.
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
Maps specific obligations to control designs, names fairness metrics they chose and why, and distinguishes high-risk from limited-risk classifications confidently.
02
Evaluation factor
Real incidents and findings
30% weight
Probe actual engagements: bias audits run, model inventories built, conformity assessment gaps found, or a deployed LLM pulled back after evaluation findings. Ask for client scale.
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
Cites named assessments with findings, affected model counts, and the concrete remediation or deployment decision that followed their report.
03
Evaluation factor
Risk judgement
20% weight
Test how they triage AI risk: hallucination in a customer-facing chatbot versus scoring bias in credit decisions, and where they accept residual risk.
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 harms by severity, exposure and reversibility, argues proportionate controls, and states plainly when a use case should not ship.
04
Evaluation factor
Getting things fixed
15% weight
Assess how they move enterprise teams: getting data scientists to log lineage, persuading legal and product owners, and embedding gates into MLOps release pipelines.
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
Describes governance boards, sign-off gates and templates they installed, plus evidence adoption persisted after the consulting engagement closed.
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