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24/06/2026 7:08 pm
TL;DR: Manual governance, spreadsheets and email approvals are creating AI technical debt as models and agents move faster than approval chains, according to Collibra. The real issue is not ambition but operating without traceability, ownership and repeatable controls that can keep pace with production AI scale.
NHIMG editorial — based on content published by Collibra: The hidden technical debt of AI: Why manual governance is slowing down your AI scale
By the numbers:
- 95% of generative AI projects never make it into production, a failure rate that points to a bigger problem than ambition.
Questions worth separating out
Q: How should security teams govern AI use cases that keep changing after approval?
A: Treat approval as the start of governance, not the finish.
Q: Why do manual AI governance processes slow down production scale?
A: Because they rely on human stitching across documents, email and tickets after the system has already changed.
Q: What breaks when AI governance cannot follow runtime changes?
A: The organization loses the ability to prove what is approved, who owns it and whether the current behaviour still matches the original risk decision.
Practitioner guidance
- Inventory AI assets as governed identities Track every AI use case, model and agent with an owner, policy state, risk rating and monitoring status so the record survives change.
- Replace spreadsheet approvals with workflow-based evidence Route approvals, policy checks and lineage updates through systems that retain the full chain of trust rather than separate documents.
- Trigger review on material AI change events Reassess governance when the dataset changes, the model is updated, the agent gains new actions or the audience expands.
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 end-to-end operating model for connecting AI use cases to policies, owners and monitoring state.
- The specific workflow steps the vendor says reduce manual evidence gathering during AI governance reviews.
- The product framing around AI Command Center and how it is positioned for governance operations.
- The customer example describing governance across 400 AI use cases and 2,000 users.
👉 Read Collibra's analysis of why manual AI governance slows production scale →
AI governance debt and production scale: what is breaking?
Explore further
25/06/2026 4:18 am
Manual governance is now a form of identity technical debt. The article describes a familiar pattern: humans can approve one use case manually, but they cannot keep up with hundreds of evolving AI assets. That is the same structural problem NHI programmes face when service accounts, tokens and agents are tracked in disconnected systems. The implication is that governance stops being a control process and becomes accumulated operational debt.
A few things that frame the scale:
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared to nearly 1 in 4 for securing human identities, according to The State of Non-Human Identity Security.
- Lack of credential rotation is cited as the top cause of NHI-related attacks by 45% of organisations, followed by inadequate monitoring and logging at 37% and over-privileged accounts at 37%.
A question worth separating out:
Q: How do identity teams apply lifecycle thinking to AI governance?
A: Use onboarding, review, exception handling and offboarding as lifecycle events for AI systems, just as you would for service accounts or other non-human identities. That approach makes ownership and accountability continuous instead of relying on a one-time approval that quickly becomes outdated.
👉 Read our full editorial: AI governance technical debt is slowing production scale
