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24/06/2026 7:34 pm
TL;DR: Open Semantic Interchange and governed semantic layers aim to make data definitions, lineage, and access controls consistent across Snowflake and Collibra workflows, which matters as unstructured data and AI models depend on trusted context, according to Collibra. The deeper shift is that governance now has to travel with the data and the semantics, not sit beside them.
NHIMG editorial — based on content published by Collibra: Accelerating data delivery using OSI with Snowflake and Collibra
By the numbers:
- Only 5.7% of organisations have full visibility into their service accounts.
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
- 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.
Questions worth separating out
Q: How should governance teams manage semantic consistency across data platforms and AI tools?
A: Governance teams should treat semantic consistency as a control objective, not a documentation exercise.
Q: Why do data governance and IAM teams need to work together on semantic layers?
A: Because access control and meaning control now overlap.
Q: How can organisations tell whether policy translation into warehouse controls is working?
A: Look for consistency between policy intent, platform enforcement, and actual user experience.
Practitioner guidance
- Inventory semantic dependencies across key data products Map the metrics, dimensions, and governed definitions that feed executive dashboards, AI models, and operational reports.
- Translate policy language into enforceable warehouse controls Validate that plain-language access rules are represented in row access policies, dynamic masking, and any downstream enforcement logic.
- Treat semantic lineage as part of assurance evidence Include metric provenance, source facts, and transformation logic in governance reviews so audit teams can verify that business meaning has not drifted across tools.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- How the OSI model is represented in Snowflake and Collibra workflows for implementation teams
- Examples of how semantic views, row access policies, and masking logic are chained together
- The specific accelerator concepts Collibra says can generate OSI and Snowflake Semantic View documents
- The full narrative Collibra uses to explain how AI models consume governed context in practice
👉 Read Collibra's analysis of open semantic interchange and data governance →
Open semantic interchange: what it means for governance teams?
Explore further
25/06/2026 4:56 am
Open semantic interchange is becoming a governance control point, not a convenience layer. The article shows that enterprises are no longer just moving data between systems. They are moving meaning, policy, and trust across those systems. Once semantic context is relied on by dashboards and AI workflows, a broken definition becomes an access problem, a quality problem, and an accountability problem at the same time. Practitioners should treat semantic interoperability as part of governance architecture, not a downstream integration detail.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to Ultimate Guide to NHIs.
- Another finding from the same research shows that 97% of NHIs carry excessive privileges, which broadens the attack surface even when governance exists on paper.
A question worth separating out:
Q: What should security teams do when AI models depend on governed business definitions?
A: Security teams should require those definitions to be versioned, reviewed, and tied to lineage before they are used in production models. AI systems amplify semantic mistakes because they reuse context at scale. If the governing definition is wrong or unclear, the model can be accurate technically and still be wrong operationally.
👉 Read our full editorial: Open semantic interchange changes how data governance scales
