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11/06/2026 10:52 pm
TL;DR: Vendor-neutral semantic metadata, consistent definitions, and portable governed results across BI and AI tools are gaining traction as Collibra’s participation in the Open Semantic Interchange points to a broader push for interoperability, according to Collibra. The real issue is not tool interoperability alone, but whether governance can survive fragmented definitions without creating new trust and accountability gaps.
NHIMG editorial — based on content published by Collibra: the Open Semantic Interchange and semantic governance
Questions worth separating out
Q: How should security teams govern shared data definitions across BI and AI tools?
A: Security teams should treat shared data definitions as governed assets with ownership, versioning, approval, and lineage.
Q: Why do inconsistent semantics create risk for IAM and AI governance?
A: Inconsistent semantics cause different systems to make decisions from different interpretations of the same term.
Q: What breaks when data definitions are shared without ownership?
A: Without ownership, changes to definitions spread faster than governance can track them.
Practitioner guidance
- Inventory the semantic terms that drive policy and reporting Map the business definitions that feed dashboards, approvals, fraud rules, and AI prompts, then identify where the same term has different logic across systems.
- Assign accountable owners for each governed definition Require an owner, version history, and approval path for every critical metric or business term so changes cannot propagate silently into analytics or automation.
- Tie semantic changes to audit evidence and lineage Record which systems consume each definition, what changed, and when it changed so auditors and control owners can reconstruct decisions later.
What's in the full analysis
Collibra's full article covers the operational detail this post intentionally leaves for the source:
- The initiative context and partner ecosystem behind the Open Semantic Interchange effort.
- How Collibra describes semantic metadata exchange across dashboards, notebooks, and AI workflows.
- The vendor's own explanation of why a vendor-neutral semantic model matters for governed analytics.
- The partnership framing and the broader ecosystem message that sits behind the announcement.
👉 Read Collibra's announcement on the Open Semantic Interchange →
Open semantic interchange: what it means for data and AI governance?
Explore further
12/06/2026 7:16 am
Semantic sprawl is becoming a governance risk, not just a data quality issue. When the same metric means different things across tools, teams lose the ability to prove that decisions were made on consistent evidence. That matters for audit, model governance, and access governance alike, because every control downstream depends on the integrity of the shared definition. Practitioners should treat semantic consistency as a control surface, not a documentation task.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap.
A question worth separating out:
Q: How do organisations know whether semantic governance is actually working?
A: Semantic governance is working when critical definitions are consistent across tools, changes are approved, and lineage can explain how a decision was derived. If users still argue about what a metric means, or if AI outputs vary because upstream terms drift, the governance layer is not effective.
👉 Read our full editorial: Open semantic interchange standardizes data semantics for AI governance
