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
Define once. Apply everywhere. Steer continuously.
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AI framework
Ensure proactive AI readiness
AI initiatives stall when rules are unclear, approvals are manual and risk ownership is undefined.
Establish a consistent framework that defines expectations upfront—so AI use cases are ready for production, not blocked by late-stage reviews.
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AI visibility
Regain control of the AI wild
As AI models and agents multiply across teams and platforms, visibility quickly erodes. Centralize AI assets to bring structure, ownership and lifecycle control—without slowing teams down.
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Executive oversight
Scale with real-time AI oversight
When AI activity grows, leadership lacks a consolidated view of what’s running and where risks concentrate. Gain real-time insight into AI systems to identify exceptions early and steer AI safely at scale.
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AI agents control
Put guardrails on AI agents before autonomy turns into risk
AI agents introduce new autonomy, faster execution and new risks.
As agents act across systems, tools and data, leaders need continuous visibility into what agents are running, how they behave and where intervention is required to keep AI under control at scale.
A single platform to deliver trusted AI at scale
AI foundation
AI registries
Centralize AI use case models and agents in registries. Track ownership, purpose, risk, lineage and status to move faster into production with clarity, accountability and trust.
Continuous traceability
End-to-end automated traceability
Automatically trace AI systems from data and policies to models, agents, and downstream business impact.
Agent governance
Agent oversight and control
Track AI agents, autonomy levels and behavior across systems—before they act beyond control.
AI workflow
AI lifecycle tracker
Apply consistent reviews, approvals and transitions from idea to production across AI initiatives including signoff.
AI
AI model integrations
Collibra connects directly to leading AI platforms—including AWS Bedrock and SageMaker, Google Vertex AI, Azure ML and AI Foundry, Databricks Unity Catalog, MLflow and SAP AI Core—to apply consistent governance where AI is built and deployed.
AI
AI compliance and risk readiness assessment
Accelerate EU AI Act and NIST AI RMF readiness with automated assessments, risk classification and clear governance to ensure accountability, oversight and compliant AI at scale.
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
The road to Data Confidence™. Get started today.
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