When organizations deploy pre-trained LLMs, they typically rely on benchmark scores to assess model safety and capability. What most teams don't realize is that these benchmarks were designed to push models forward on narrow tasks, not to evaluate whether a model is safe to deploy in your environment. Treating benchmark scores as risk assessments creates a dangerous blind spot in enterprise AI governance.

In BenchRisk (NeurIPS 2025), my co-authors and I applied NIST's risk management framework to analyze 26 widely used LLM benchmarks, identifying 57 failure modes that can lead organizations to unsupported conclusions about model safety. A benchmark score that says "safe" may be hiding gaps in coverage, reproducibility, correctness, or data contamination, gaps that become real vulnerabilities once the model is in production.

I've evaluated over 30 open-weight models for safety and performance, deploying them for customer in regulated industries where an unreliable model isn't just an engineering problem, it's a compliance and security risk. The biggest lesson: bigger is not always better. A model that leads the leaderboard can underperform a smaller model on your specific task, fail under your hardware constraints, or introduce risks that no benchmark was designed to catch.

This talk will help security and AI leaders develop a thorough understanding of how to evaluate models for deployment risk. I'll walk through real failure cases from regulated environments, show where benchmarks create false confidence, and share a practical risk mitigation framework that accounts for task fit, infrastructure constraints, safety requirements, and something rarely discussed, the true cost of doing this evaluation well.