Delivering AI you can trust — Platform Demo
From one-off AI projects to repeatable execution with continuous oversight
The final goal that we want to achieve with the AI governance is actually building trustworthy AI by design, right from the beginning
Kevin Falkenstein
Kevin Falkenstein
Cloud Architect and Data Scientist, Siemens AG
Realize your AI ambition.
Your key to enterprise AI success starts here.
- If you don’t rely on solid foundations, every AI use case becomes a one-off
Establish a standard execution blueprint covering data, models, agents, ownership, and downstream impact—before AI reaches production. - If you don’t apply controls consistently, AI execution won’t scale
Replace ad-hoc reviews with structured, repeatable execution paths that enforce ownership, policy, and lifecycle standards across AI assets. - If you don’t steer AI continuously, value and risk drift
Monitor trust, performance, and risk impact to prioritize action and guide AI at scale.
No matter if you are on the cybersecurity team, you’re in risk, you’re in compliance, you can go right into Collibra versus going to these disparate tools.
John Tucker
John Tucker
Director of Enterprise Data Governance, McDonald's
Define once. Apply everywhere. Steer continuously.
Achieving AI success at enterprise scale
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Define a repeatable AI execution pattern
- Define a standard blueprint for AI use cases, including lineage across data, models, agents and downstream impact
- Embed data quality and integrity expectations to ensure AI is built on reliable, trusted inputs
- Clarify ownership, risk checks and success criteria so every AI initiative starts with the same structure
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Apply and control AI assets consistently across AI cycle
- Connect AI environments and platforms to continuously collect metadata, lineage and operational signals
- Enrich AI assets with business, technical and regulatory context, including ownership, usage and lifecycle status
- Replace ad-hoc approvals with structured, scalable execution paths across AI assets
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Continuously steer AI systems at scale
- Monitor trust, performance and impact across AI systems in real time
- Identify risk concentration, value drift and exceptions requiring intervention
- Prioritize investments and steer AI execution dynamically through live signals
Frictionless execution at scale
Standardize how AI is built and governed, enabling teams to scale faster with less friction
De-risked compliance readiness
Embed regulatory and policy alignment early to avoid delays and costly remediation later
Avoided downstream control costs
Maintain traceable, transparent AI systems that scale without hidden operational or organizational debt
Accelerator
AI Governance accelerator: Get production-ready, compliant AI fast
A four-week expert-led engagement to jump-start AI governance. Capture AI use cases and models, configure roles, workflows and assessments, and integrate with leading ML platforms. Establish scalable oversight, compliance and collaboration across the full AI lifecycle.
Resources and insights
Solution brief
Delivering AI you can trust: Define, Control, and Steer AI Act-Ready Systems
On-demand webinar
Governing AI agents before they govern you
Ebook
How to govern AI agents at scale: The pitfalls, promises and the future of work in the age of agentic AI
Whitepaper
Collibra AI Governance: Your competitive advantage for AI use cases
The road to Data Confidence™ starts here.
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