Topic starter
23/06/2026 6:59 pm
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.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “AI needs context: Why LLMs still don’t understand real-world systems”.
Key questions
Q: How should teams keep AI systems from selecting the wrong dataset in governed workflows?
A: Treat dataset authority as a control problem, not a prompt-quality problem.
Q: Why do plausible AI outputs still create operational risk?
A: Because plausibility is not the same as correctness in a governed environment.
Q: What signals show that data product governance is not mature enough for AI use?
A: Warning signs include unclear ownership, manual handoffs, poor documentation, and no reliable way to inspect dependencies or outputs.
Practitioner guidance
- Define authoritative context sources Catalog which datasets, workflows, and policy records are authoritative for each AI use case, and mark outdated or non-canonical sources so agents cannot treat them as equivalent.
- Attach governance metadata to AI decision paths Expose certification, ownership, classification, and policy constraints wherever an agent or assistant can choose among multiple inputs or actions.
- Restrict agent actions to governed data paths Require AI systems to verify trust signals before selecting data or triggering downstream workflows, rather than allowing free choice from all accessible sources.
Bottom line: LLMs can generate convincing guidance while still missing the authoritative state of a governed environment.
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25/06/2026 2:45 am
Governed context is the missing control plane for AI decisioning: LLMs can reason over language, but they cannot natively distinguish authoritative enterprise state from plausible text. That makes context a governance function, not a usability enhancement. When AI is allowed to act inside business workflows, the practitioner problem becomes controlling the trust signal behind the action, not polishing the prompt.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: Should organisations let AI agents trigger workflows without trusted context checks?
A: No. Once an agent can act, trusted context becomes part of the authorisation decision. Organisations should require policy-backed context before a dataset is selected or a workflow is triggered, because post-action review cannot reliably catch a decision that looked valid at runtime but was based on the wrong source.
👉 Read our full editorial: AI needs governed context, not just better language outputs