Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety before deployment. Without them, you’re guessing.
But in regulated domains such as healthcare, finance, and government, data scarcity and privacy constraints make building benchmarks incredibly difficult. Real-world data is locked behind confidentiality agreements, is fragmented across silos, or is prohibitively expensive to annotate. The result? Innovation stalls, and evaluation becomes guesswork. For example, government agencies deploying AI assistants for citizen services—like tax filing, benefits, or permit applications—need robust evaluation benchmarks without exposing personally identifiable information (PII) from real citizen records.
This blog introduces an AI-driven, privacy-preserving evaluation workflow that can be applied across industries to benchmark LLMs safety and efficiency. We’ll use a healthcare example to illustrate the process, but the same approach works for any domain where data privacy is critical. You’ll learn how to generate domain-specific synthetic datasets in minutes using NVIDIA NeMo Data Designer and build reproducible benchmarks with NVIDIA NeMo Evaluator—without exposing a single real record.
