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24/06/2026 7:22 pm
TL;DR: AI systems increasingly fail not because they lack data, but because they lack organisational context, making outputs harder to trust, explain, and operationalise in production according to Collibra. The governance issue now sits in metadata, lineage, and runtime decision support, where identity, data, and AI controls meet.
NHIMG editorial — based on content published by Collibra: AI needs context, why data alone is not enough
Questions worth separating out
Q: How should teams govern AI systems that need organisational context to make decisions?
A: Teams should treat context as part of the decision system, not as a separate documentation layer.
Q: Why do AI systems fail even when the underlying data is accurate?
A: Accurate data is not enough when the system does not understand how that data fits into the organisation.
Q: What signals show that an AI governance model is missing context controls?
A: Common signals include repeated manual validation, inconsistent outputs across environments, conflicting interpretations of the same dataset, and growing reliance on human review to interpret model results.
Practitioner guidance
- Map context dependencies before AI is allowed to act Identify which datasets, business definitions, ownership records, and policy signals an AI system needs before it can make or trigger a decision.
- Expose governance metadata at runtime Make certification status, ownership, data quality state, and usage constraints available inside the decision path so the system can evaluate them before calling tools or publishing outputs.
- Align AI controls with identity and policy enforcement Connect access checks, approval logic, and policy constraints to the same execution path that consumes data.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- The article's explanation of how the Collibra MCP server surfaces context to AI agents at runtime.
- The vendor's description of how certified data, ownership, and quality signals are queried before action.
- The product framing around metadata as a runtime control layer for decision-making workflows.
- The next-article teaser on how large language models still fail to understand systems and dependencies.
👉 Read Collibra's analysis of why AI needs context beyond data volume →
AI context gaps: what do IAM and governance teams need to know?
Explore further
25/06/2026 4:37 am
Context failure is now an identity governance problem, not just a data management issue. Once AI systems can call tools and trigger workflows, the question is no longer whether the model can read data, but whether it can interpret that data inside a governed decision path. Metadata, ownership, lineage, and policy state become controls that shape action, not just records that describe it. Practitioners should treat context as a runtime governance dependency, not an optional data layer.
A few things that frame the scale:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, according to The 2024 ESG Report: Managing Non-Human Identities.
- Two-thirds of enterprises have endured a successful cyberattack resulting from compromised non-human identities, with a quarter encountering multiple attacks.
A question worth separating out:
Q: How do metadata and access governance work together in AI programmes?
A: Metadata tells the system what the data means and how it should be used, while access governance determines whether the system is allowed to use it at all. In AI programmes, both layers need to meet at runtime so policy, ownership, and trust signals are available when the decision is made.
👉 Read our full editorial: AI context gaps are now an enterprise governance problem
