NVIDIA Generative AI Platform Review
Purpose
Review NVIDIA generative-AI platform posture against the NCA-GENL, NCA-GENM, and NCP-GENL bodies of knowledge: NeMo training/customization pipelines, NIM inference microservice deployment, NeMo Guardrails configuration, model card and weights provenance, evaluation harness coverage, and tenant data isolation in multi-tenant inference.
Lean operating rules
- Prefer live evidence (
cosign verify nvcr.io/nim/..., NIM/v1/health/ready, NeMo Guardrails config files, model card YAML, eval harness reports) when the active client exposes it; otherwise fall back to NVIDIA NeMo / NIM documentation and sanitized configuration. - Separate confirmed facts from inference. If NIM image signature verification, guardrails state, or eval coverage was not directly queried, say so.
- Treat NIM container pulled from
nvcr.iowithoutcosign verifyagainst NVIDIA's published key/identity as a high finding — image trust is unverified. - Treat absence of model card or weights provenance attestation for production-served models as a high finding — lineage cannot be reconstructed.
- Treat NeMo Guardrails absent or bypassable for an externally exposed LLM endpoint as a critical finding for any regulated workload.
- Treat evaluation harness covering only loss/perplexity but no safety, jailbreak, or hallucination evaluation as a high finding for customer-facing LLMs.
- Treat training data with no documented PII handling, no opt-out path, and no retention bound as a critical finding for regulated tenants.
- Treat NIM inference shared across tenants without per-tenant request scoping as a high finding — cross-tenant context leakage path.
Response minimum
Return, at minimum:
- the scoped target (NeMo pipeline, NIM microservice, model class) and evidence level,
- NIM image signature verification posture,
- guardrails posture,
- model card / weights provenance posture,
- eval harness coverage,
- tenant isolation posture,
- safe next actions and assumptions or blockers.