AI platform trust: are your model and agent controls enough?

TL;DR: A trusted AI platform is defined by seven enforceable controls, including a single inventory, governed context, runtime control, observability, lineage, policy as code, and continuous audit evidence, according to Collibra. The key issue is not AI branding but whether governance survives at runtime, when agents can act before humans review them.

NHIMG editorial — based on content published by Collibra: What makes an AI platform 'trusted'? 7 non-negotiables for governing models and agents

Questions worth separating out

Q: How should security teams govern AI platforms that run both models and agents?

A: Start with a complete inventory, then require governed context, runtime enforcement, observability, lineage, policy as code, and continuous audit evidence.

Q: Why do agents raise the bar for AI governance and identity controls?

A: Agents can act continuously and take consequential actions without a human in the loop, so launch-time review is not enough.

Q: What do organisations get wrong about trusted AI platforms?

A: They often treat trust as a label or a dashboard score instead of a set of enforceable controls.

Practitioner guidance

• Inventory every model and agent Create a single source of record with owner, risk tier, data access scope, and business purpose for every AI system before it reaches production.
• Enforce policy at runtime Move access, masking, and retention rules into code that evaluates when the AI reaches for data, not only when the system is reviewed.
• Require traceable decision evidence Capture decision traces, data-access events, and lineage from source to model input to agent action so each outcome can be reconstructed under audit or incident review.

What's in the full article

Collibra's full blog post covers the operational detail this post intentionally leaves for the source:

• How Collibra maps each non-negotiable to platform capabilities and operating assumptions
• The exact wording of the seven trust criteria and the comparison table used in the article
• The KU Leuven accuracy comparison and how the vendor frames governed context in practice
• The FAQ section’s full explanation of how to evaluate a platform against the seven controls

👉 Read Collibra's analysis of what makes an AI platform trusted →

AI platform trust: are your model and agent controls enough?

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