Nvidia’s NemoClaw has three layers of agent security. None of them solve the real problem.

Nvidia's layered security approach for autonomous agents highlights a critical gap in how we're architecting LLM systems in production. From my experience deploying data pipelines with agentic components, the real vulnerability isn't at the application layer—it's in the data access and observability patterns we inherit from legacy data platforms. When agents execute queries against your data warehouse, sandbox technology means little if your IAM model treats the entire lakehouse as a single trust boundary. The industry is moving toward agentic workflows faster than our governance infrastructure can scale. What we need isn't better agent sandboxing but comprehensive lineage tracking and just-in-time access patterns that treat every LLM inference as a potential data exposure vector. Teams building modern data stacks should immediately audit their data permissions model, implement column-level access controls, and establish audit logging that captures agent decision trails—not to satisfy compliance, but to understand failure modes before they compound at scale.