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24/06/2026 8:48 pm
TL;DR: Cloud migrations fail when governance fragments across policies, roles, metadata and access reviews, creating cloud chaos, data sprawl and AI-driven risk, according to Collibra. The central lesson is that migration speed without unified control turns identity, data and compliance gaps into operational debt, not strategic advantage.
NHIMG editorial — based on content published by Collibra: From cloud chaos to strategic command: The governance reset for enterprise migration
By the numbers:
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption.
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job.
Questions worth separating out
Q: How should security teams govern cloud migrations without losing access control context?
A: Security teams should connect asset ownership, sensitivity and entitlements in one workflow before cutover.
Q: Why do lift-and-shift migrations create hidden identity and compliance risk?
A: Lift-and-shift often moves systems faster than governance can classify them.
Q: How do you know if cloud governance is actually working?
A: Cloud governance is working when every important asset has a current owner, an explicit policy, a review path and a traceable exception history.
Practitioner guidance
- Build a unified asset ownership map Link each migrated dataset or workload to a named owner, sensitivity label and policy authority before it is moved.
- Embed access review into migration workflow Require identity review at the same stage as workload readiness so entitlements, temporary exceptions and inherited permissions are checked together.
- Curate lineage with control relevance Track where data came from, who transformed it and which policy applies at each hop, so reviewers can tell whether a dataset is still fit for its intended use.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- The vendor's step-by-step operating model for defining ownership, stewardship and risk management across migration phases.
- Detailed guidance on discovering and classifying data sources before cloud cutover, including how to curate data assets by criticality.
- The article's readiness checks for data quality, sensitivity and compliance before migration decisions are made.
- Examples of how Collibra frames policy, metadata and workflow centralisation for cloud governance at scale.
👉 Read Collibra’s cloud migration governance analysis →
Cloud migration governance: what IAM teams are missing?
Explore further
25/06/2026 5:37 am
Cloud migration exposes an identity governance problem before it exposes a technology problem. When roles, policies and metadata are split across teams, access decisions lose the context needed for control enforcement. The result is not simply administrative confusion. It is a governance model that cannot reliably answer who owns the data, who approved the access, or whether the entitlement still matches the migration state. Practitioners should read cloud migration as a lifecycle and accountability issue, not a platform change.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to The 2026 Infrastructure Identity Survey.
- A further 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to The 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What should IAM teams do when AI starts using migrated cloud data?
A: IAM teams should treat AI-enabled access as part of the same governance boundary as the cloud workload. If AI systems can consume, transform or act on migrated data, then access scope, lineage and accountability must be reviewed together or the organisation will lose control of the decision chain.
👉 Read our full editorial: Cloud migration governance reset for enterprise data and AI risk
