Topic starter
24/06/2026 8:56 pm
TL;DR: After evaluating 10 vendors across 19 criteria, Forrester named Collibra a Leader in Data Governance and a Strong Performer in AI Governance, highlighting how enterprises are merging data control, policy management, and AI oversight under one governance model. The bigger issue is that governance stacks are converging faster than most operating models can absorb.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “Collibra receives dual recognition in Forrester Waves: A Leader in Data Governance, A Strong Performer in AI Governance”.
Key questions
Q: How should teams govern AI assets and data together without creating duplicate controls?
A: Build a single governance inventory that links datasets, models, policies, and approvals, then reuse governance artefacts only when ownership, versioning, and review evidence remain intact.
Q: Why does an AI asset catalogue matter for governance?
A: Because it is where policy status, lineage, approved inputs, and monitoring evidence become usable in operational decisions.
Q: Why do data governance and AI governance need to be connected?
A: Because AI systems depend on data quality, data location and data access, so weak data governance creates blind spots in both model risk and privacy exposure.
Practitioner guidance
- Define a shared governance operating model Map which decisions are common across data governance and AI governance, then assign one accountable owner for policy, exception handling, and evidence quality.
- Audit the AI asset catalogue Check whether each AI asset record includes approved inputs, lineage, policy status, and monitoring evidence needed for operational decisions.
- Align review and certification cadence Make sure governance reviews, compliance checks, and exception renewals happen on a schedule that covers both datasets and AI use cases.
Bottom line: Data governance and AI governance are converging into a single operating challenge, and separate control paths create avoidable gaps.
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25/06/2026 5:50 am
Governance convergence is now an operating model issue, not a tooling preference. When data governance and AI governance are managed separately, organisations duplicate policy logic and create inconsistent evidence paths. The article shows that enterprises are increasingly expecting one governance model to cover both data assets and AI assets, which means control ownership and review cadence must be unified. Practitioners should treat convergence as a governance design problem, not a product selection exercise.
A few things that frame the scale:
- 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:
A: Security leaders should treat AI as a governed capability, not a standalone tool. Start with clear data boundaries, strict access control, and lifecycle rules for collection, use, retention, and disposal. Then require monitoring for model drift, bias, and false outputs. The goal is to improve detection and response while preserving privacy, accountability, and auditability across the entire security program.
👉 Read our full editorial: Forrester’s dual recognition of Collibra and what it signals