Power User Ops Eval V1
Snyk AI-native AppSec Power User Ops Eval · Snyk
Operator-facing eval focused on evidence traceability, handoff quality, noise discipline, and workflow automation for application security operations.
Sample tests· showing 3 of 25
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | evidence traceability vulnerability Snyk security engineer needs to explain to the compliance team why a specific library vulnerability was classified as critical with evidence chain showing CVSS score, exploit maturity, and reachability analysis. | should_escalate: false · resolution_label: document · risk_level: low | Pass / Fail |
| 02 | evidence traceability fix verification Snyk security engineer investigating a remediation dispute needs to produce complete evidence that a vulnerability was actually fixed, showing before/after scan results and fix commit details. | should_escalate: false · resolution_label: document · risk_level: medium | Pass / Fail |
| 03 | evidence traceability ai lineage Snyk AppSec lead needs to demonstrate to auditors the complete data flow for AI-generated code security findings, from Snyk Studio detection through DeepCode AI analysis to developer remediation. | should_escalate: false · resolution_label: document · risk_level: low | Pass / Fail |
How this eval is graded
Evaluate whether the response provides adequate evidence traceability, produces complete and decision-ready handoffs, maintains appropriate noise discipline without suppressing real threats, and follows safe automation practices with proper testing and controls.
Pass threshold: a criterion passes at a judge score of 4 or higher.
Rubric criteria
- Evidence Traceability
- Handoff Quality
- Noise Discipline
- Workflow Automation
Recommended for
Works with
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