Look for the ability to direct the tool, critique its output, and decide what should not be built. AI can accelerate routine implementation, but it cannot replace engineering judgement about architecture, constraints, and validation. The best signal is whether the candidate treats the model as an assistant and not as a substitute for technical responsibility.
Why This Matters for Security Teams
Hiring for AI-assisted engineering is not just about speed with a code assistant. The real question is whether the engineer can keep control when the model suggests unsafe patterns, missing edge cases, or an implementation that violates architecture and policy. That judgement matters because AI tools can make low-skill output look productive while quietly increasing exposure to secrets, insecure dependencies, and unreviewed changes. NIST’s NIST Cybersecurity Framework 2.0 still applies: teams need governance, risk awareness, and verification, not just faster delivery.
NHIMG’s The State of Secrets in AppSec shows why this is not theoretical. If developers already struggle with secrets discipline, adding AI assistance can amplify bad habits unless the engineer knows when to stop, inspect, and reject the model’s suggestion. In practice, many security teams discover the gap only after an AI-generated shortcut has already been merged into production.
How It Works in Practice
Effective evaluation should focus on how a candidate uses the tool, not whether they can prompt it to produce something impressive. Strong engineers can give clear intent, compare alternatives, and challenge output that is technically correct but operationally wrong. They should be able to explain what context the model does not have, which constraints matter most, and how they would validate the result before shipping.
Practical interviews can test this with a real task and a deliberately imperfect AI-generated answer. Ask the candidate to:
- identify incorrect assumptions in the output
- spot security, reliability, or maintainability risks
- decide whether the feature should be built at all
- show how they would verify the code with tests, reviews, or policy checks
- describe what they would never allow a tool to do without human approval
This maps to current guidance from the NIST Cybersecurity Framework 2.0 and the operational lessons in DeepSeek breach: speed without judgement is a liability. A good signal is whether the engineer treats the model as a drafting aid, then uses their own reasoning to decide on architecture, security boundaries, and review criteria. These controls tend to break down when teams equate fluent AI output with engineering competence because the review process then rewards confidence over correctness.
Common Variations and Edge Cases
Tighter evaluation often increases interview time and makes hiring feel less standardized, so organisations need to balance consistency against the need to observe real judgement. That tradeoff is especially visible for senior engineers, platform specialists, and security-sensitive roles, where the key skill is often not producing code quickly but refusing the wrong solution.
Current guidance suggests different tests for different roles. For application engineers, the focus may be on code review, test quality, and scope control. For platform or infrastructure engineers, it may be on how they constrain tool output around access, deployment, and rollback safety. For security engineers, the most important signal is whether they can see when AI assistance creates hidden exposure, especially around secrets handling and unsafe automation.
There is no universal standard for this yet, but strong practice is to ask for evidence of decision-making under uncertainty. Candidates who can explain why a feature should not be built, or how they would contain a risky AI suggestion, are usually better prepared than those who only know how to generate code. That distinction aligns with the broader risk-management mindset in NIST Cybersecurity Framework 2.0 and the developer behaviour gaps highlighted in The State of Secrets in AppSec.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0, NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM-01 | AI tool use needs risk judgement, not just output quality. |
| NIST CSF 2.0 | PR.IP-1 | The question is about disciplined development practices with AI tools. |
| NIST AI RMF | Judging AI-assisted work depends on governance and human oversight of model use. |
Set explicit risk criteria for AI-assisted coding and require human review before release.
