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23/06/2026 7:20 pm
TL;DR: Most organisations can run AI pilots, but many cannot govern fifty or more systems in production because documentation, ownership, lineage, and review processes collapse under scale, according to Collibra. The real failure is governance debt: visibility gaps, accountability gaps, and compliance gaps turn AI portfolios into unmanaged risk.
NHIMG editorial — based on content published by Collibra: Enterprise AI governance: How to scale safe and compliant AI across the organization
By the numbers:
- Organisations that describe themselves as confident in their AI deployment actually experience a 72% security incident rate, compared to 33% for those who remain cautious.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems.
Questions worth separating out
Q: How should organisations govern AI systems once they move from pilots to production?
A: They should manage AI as a portfolio with named ownership, documented data lineage, risk classification, and continuous review.
Q: What breaks when AI governance is treated as a one-time project review?
A: What breaks is accountability.
Q: When should teams tie AI governance to data governance?
A: They should do it from the start, because model trust depends on the data used to train and operate the system.
Practitioner guidance
- Create a production AI inventory Register every AI use case, model, and agent in one governed system with named owners, business purpose, data sources, and review status.
- Tie approvals to lineage evidence Require every approved AI system to link to its training data, operational inputs, and quality checks so changes can be traced back to the source.
- Automate re-review triggers Set governance workflows to reopen assessment when data changes, ownership changes, or a model is retrained.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- A closer breakdown of how the Collibra AI governance system of record connects inventory, lineage, and compliance workflow.
- The article's explanation of how teams register AI use cases at intake and maintain living documentation as systems change.
- More detail on how data governance and AI governance are linked through lineage, quality monitoring, and audit-ready records.
- The source's discussion of how regulatory reporting changes when oversight becomes continuous rather than retrospective.
👉 Read Collibra's analysis of enterprise AI governance at scale →
Enterprise AI governance at scale: are your controls keeping up?
Explore further
25/06/2026 3:17 am
Governance debt is the right name for the enterprise AI problem. The article is describing more than process sprawl. It is describing a control environment where project-era reviews, undocumented ownership, and stale risk decisions accumulate until they are no longer usable at scale. The implication for the field is that AI governance has to be treated as a living operating discipline, not a one-time approval step.
A few things that frame the scale:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to The 2026 Infrastructure Identity Survey.
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to The 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Who should be accountable when an AI system creates compliance risk?
A: Accountability should be explicit across the business owner, model owner, data owner, and compliance function, with one party responsible for the final governance record. If everyone shares responsibility, nobody owns the outcome. Clear accountability is what turns AI oversight from a discussion into a control.
👉 Read our full editorial: Enterprise AI governance at scale: why pilot-era controls fail
