Topic starter
24/06/2026 9:08 pm
TL;DR: Data governance is becoming a control plane for AI readiness, not just a reporting function, according to Forrester, which named Collibra a Leader in its Data Governance Solutions Wave after assessing 13 vendors across 28 criteria, with the report highlighting AI governance, lineage analysis, observability, and policy modelling as key differentiators. The result matters because data governance is becoming a control plane for AI readiness, not just a reporting function.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “Collibra named a Leader in The Forrester Wave ™ : Data Governance Solutions, Q3 2025”.
Key questions
Q: How should security teams govern data access for AI workloads?
A: They should govern AI data access by business purpose, dataset classification, and downstream reuse, not by repository alone.
Q: Why do lineage and observability matter for governance decisions?
A: Because policy decisions are only defensible if teams can see where data came from, how it changed, and who used it.
Q: What breaks when governance cannot model data reuse across AI use cases?
A: Teams lose the ability to explain how data can be shared safely, which roles inherit risk, and what downstream consumers are affected by a policy change.
Practitioner guidance
- Map governance controls to access decisions Identify where data governance decisions affect who can use data, which workloads can consume it, and how AI use cases inherit those permissions.
- Prioritise lineage and usage visibility Require traceability from source to downstream consumer so policy reviews can account for transformations, sharing, and reuse across platforms.
- Model policy for AI reuse explicitly Define how governed data may be observed, shared, and reused in AI workflows before those use cases expand beyond the original access model.
Bottom line: The article shows that data governance is being recast as a control layer for AI readiness and governed reuse, not just a reporting discipline.
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25/06/2026 6:10 am
Data governance is becoming the policy substrate for AI access decisions. The article reflects a broader market shift: governance platforms are being asked to define not just data quality and stewardship, but the conditions under which data can safely feed analytics and AI. That makes governance inseparable from identity and access design, because policy now reaches into both human use and machine consumption. Practitioners should treat governance as an enforcement layer, not a documentation exercise.
A question worth separating out:
Q: How can security and IAM teams keep governance from becoming a one-time project?
A: Treat governance as an operating model that needs monitoring, reinforcement, and periodic expansion. Set thresholds for coverage, require named ownership, and review whether teams still follow the process after the initial rollout. If the process only works when specialists are pushing it, adoption is incomplete.
👉 Read our full editorial: Forrester’s data governance wave signals a wider AI governance shift