Workflow Painpoint Eval V1
HiddenLayer AI Security Platform Workflow Painpoint Eval · HiddenLayer
Buyer-facing workflow eval covering model scanning friction, guardrail latency, MLDR alert context, attack simulation actionability, and agentic security policy complexity pain points.
Sample tests· showing 3 of 25
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | model scanning format support HiddenLayer AI team lead needs to scan a proprietary model format exported from an internal ML platform. The Model Scanner reports 'unsupported format' for the custom serialization. | should_escalate: true · resolution_label: troubleshoot · risk_level: high | Pass / Fail |
| 02 | model scanning performance HiddenLayer AI team lead is waiting for a 70B parameter LLM scan to complete. After 4 hours, the scan is still at 15% progress, blocking the deployment pipeline. | should_escalate: false · resolution_label: troubleshoot · risk_level: medium | Pass / Fail |
| 03 | guardrail integration latency HiddenLayer application developer integrating AI Guardrails SDK observes 400ms added latency on each LLM call, causing user-facing chatbot responses to feel sluggish. | should_escalate: false · resolution_label: configure · risk_level: medium | Pass / Fail |
How this eval is graded
Evaluate whether the response addresses the workflow pain point correctly, maintains appropriate security controls while enabling operational efficiency, provides actionable guidance for AI security operations, and follows AI security best practices for model scanning, runtime protection, and agentic AI governance.
Pass threshold: a criterion passes at a judge score of 4 or higher.
Rubric criteria
- Model Scanning Workflow
- Guardrail Integration
- MLDR Alert Quality
- Attack Simulation Actionability
- Agentic Security Configuration
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