LLMs in real workflows: where context-aware governance fails

TL;DR: LLMs can summarize and guide, but they still operate on representations of systems rather than the governed systems themselves, according to Collibra. That gap becomes operational risk when AI agents choose data or trigger workflows without trusted context, because plausible outputs can still be the wrong operational decision.

NHIMG editorial — based on content published by Collibra: AI needs context, why LLMs still do not understand real-world systems

Questions worth separating out

Q: How should teams govern AI systems that choose data in real workflows?

A: Teams should govern AI data choice the same way they govern privileged access: by defining authoritative sources, exposing certification and lineage metadata, and enforcing policy checks before execution.

Q: Why do AI assistants create risk even when access is technically approved?

A: Because access approval does not guarantee contextual correctness.

Q: What breaks when metadata is not available at decision time?

A: Decisioning becomes pattern matching without governance.

Practitioner guidance

• Classify authoritative context sources Identify which datasets, workflow states, and policy records are authoritative for each use case, then mark the rest as non-authoritative even if they remain technically accessible.
• Expose governance metadata to AI decision paths Publish lineage, certification, ownership, and policy constraints in forms that AI tools and agents can query before selecting data or triggering actions.
• Add contextual checks before workflow execution Require AI systems to validate dataset freshness, policy applicability, and approval status before they can invoke downstream processes.

What's in the full article

Collibra's full blog post covers the operational detail this post intentionally leaves for the source:

• How Collibra maps governance metadata to data, models, and processes inside AI workflows.
• The article's own explanation of how the Model Context Protocol and Collibra MCP server expose trusted context to AI systems.
• A stepwise view of how certification, ownership, and policy constraints can be embedded into operational decision paths.
• The vendor's final framing of controlled system intelligence and why context drift becomes the next governance problem.

👉 Read Collibra's analysis of why LLMs still need governed context →

LLMs in real workflows: where context-aware governance fails?

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