5 Steps to Operationalize AI Governance With Collibra

AI is now powering everything from customer service chatbots to advanced analytics in healthcare and financial services. But many organizations have already learned first-hand that setting policies around the responsible use of AI is not enough. AI governance often fails at the operational level, where silos, manual processes, and limited visibility slow down AI initiatives and increase risk. 

So, let’s talk about it and how Collibra AI Governance can support data leaders and teams in bridging the gap between policy and execution.

Why AI governance needs to move from paper to practice (and how Collibra helps)

To get it out of the way, let’s start with the definition. Gartner’s definition of AI governance is: 

“AI governance is the process of creating policies, assigning decision rights, and ensuring organizational accountability for the application and use of artificial intelligence techniques.”

But we’re not here for the theory, and neither are you. And this article is exactly about turning that theory into practice with the right tools.

Today, AI is mission-critical for many industries and organizations, helping banks detect fraud, retailers tailor customer experiences, and healthcare providers improve diagnostics. The upside of its use is massive: faster decisions, new revenue streams, and often a real competitive edge.

But whenever there’s an upside, there’s a downside, too. A big portion of AI governance involves balancing business value with risk and growing accountability. 

Regulations like the EU AI Act are changing the way organizations manage AI, while Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously by AI, up from virtually none today.

That means AI governance can no longer be just an afterthought. Policies written on paper are not enough to satisfy regulators, customers, or executives who need to trust the decisions AI makes on their behalf. And trust here is crucial.

Without a structured approach, AI is just a collection of isolated experiments, prone to model bias, data risk, and inconsistent data management practices. But when you operationalize governance, it becomes a trusted growth engine:

• Instead of hunting for scattered documentation, teams can instantly see every model, dataset, and dependency in one place.
• Compliance officers get real-time visibility into risks and automated reporting for regulations.
• Data scientists spend less time on paperwork and more time on innovation.
• Executives make decisions with confidence, knowing AI is delivering value without exposing the organization to unnecessary risk.

This is exactly what Collibra makes possible by bridging the gap between policy and execution:

• It embeds governance into daily workflows.
• It automates data quality checks and documentation.
• It creates a common governance language across enterprise teams. 

The result? AI that is not only innovative, but also transparent, scalable, and future-proof.

The Collibra AI Governance framework

You can think of Collibra AI Governance as the translator between policies on paper/screen and their real-world implementation. With Collibra’s unified governance platform, teams can govern AI and build models on trusted data and use them ethically across the board.

It’s really the difference between “we have a policy” and “we actually follow it”, turning AI governance into everyday reality.

The Collibra AI Governance framework includes four steps:

Define your use case 

Capture ideas from stakeholders, evaluate them, and define the AI use case, including the data, model(s), and purpose. Set desired mission outcomes and KPIs, assess risks, and assign ownership and accountability. 

Identify and understand data 

Collect and evaluate all the data that’s available, whether or not it’s high-quality/certified, and whether you can use it with your specific use case.

Document models and results 

Document, trace, and track the model, associated data products and usage, allowing for model analysis and reporting. 

Verify and monitor 

Verify your model and continuously monitor to maintain the quality and compliance of the underlying data products. Retrain, test, and audit models regularly.

Collibra AI Governance Framework. Source: Collibra 

Now, let’s take a look at what that reality actually looks like using the Collibra Platform. The following five steps show how you can use Collibra to turn AI governance into everyday practice, ensuring compliance, building trust, and unlocking AI’s full potential for your organization

Murdio tip: Get your data and analytics strategy in order first

Before you take the first step towards AI governance, make sure you have a solid D&A strategy and defined business goals in relation to data. As Gartner’s research shows, one of the top challenges for companies when implementing AI governance is a lack of clarity about the business impacts of AI (48%) followed by a production-first mindset, with data governance merely an afterthought 37%).

The biggest challenge, though, is skill gaps – and that’s where hiring external data governance experts with advanced Collibra skills can make a huge difference. 

Step 1: Catalog AI models and map lineage

Before you can govern, you need to know what you’re governing. Many organizations don’t even have a full picture of which AI models are running, what underlying data they rely on, or who owns them. The lack of transparency creates blind spots that can turn into serious data risk, especially when personally identifiable information is involved.

Collibra’s AI Catalog and Collibra Data Catalog give you a central inventory of every model, dataset, and dependency. You can trace lineage to see exactly which data used to train AI flows into which model and how results are produced. This creates visibility to build trust, spot model bias, and avoid relying on just a “black box.”

Quick actions with Collibra:

• Inventory all AI models and their metadata in Collibra’s catalog.
• Document datasets, including sensitive data and identifiable information.
• Visualize lineage for every model, tracking both inputs and outputs.
• Share lineage maps with data experts, legal, and compliance teams to review and approve.

Step 2: Integrate with machine learning platforms

AI is built and deployed on machine learning platforms like AWS SageMaker or Azure ML. But governance often gets left behind when models move to production. 

By syncing Collibra data with model metadata, you automatically capture important details like accuracy, model performance, and version history. This cuts out manual documentation, which drains time and creates inconsistencies. 

The ability to automatically catalog model metadata is a core function of a modern AI governance strategy. We discussed the specific role of machine learning data catalogs in this process in a separate article.

In fact, metadata-driven automation has shown measurable productivity gains. Collibra cites a reduction in data-search time from four hours per week to just 50 minutes by switching to metadata-driven automation.

With connections like this in place, legal teams and compliance officers can trust that AI documentation is reliable, and business users can focus on innovation instead of red tape.

Quick actions with Collibra:

• Connect Collibra to machine learning platforms to sync metadata automatically.
• Use automated Collibra workflows to capture model versions, audit logs, and accuracy metrics.
• Standardize documentation for audits and legal compliance.
• Give data scientists and stakeholders direct access to accurate, real-time model information.

Step 3: Automate bias detection and data quality checks

Poor-quality or biased data used to train models can lead to inaccurate predictions – or worse, discriminatory outcomes. And spotting those issues manually is nearly impossible at scale, while the impact is enormous.

Collibra tackles this with Collibra Data Quality & Observability. They allow you to set data quality rules that automatically check for anomalies, drift, or model bias in training datasets. Continuous monitoring is necessary to make sure that when new data flows in, you’re not compromising accuracy or compliance.

Collibra Data Quality & Observability tools. Source: Collibra documentation

In industries like healthcare, checks like this are critical. Automated governance helps safeguard against skewed patient outcome models or unfair predictions, supporting the responsible use of AI across critical AI applications.

Quick actions with Collibra:

• Define automated data quality rules to monitor training sets.
• Set up continuous observability dashboards for data and analytics.
• Detect and remediate model bias early in the AI lifecycle.
• Keep a record of corrective actions for legal and ethical accountability.

Step 4: Enable cross-team collaboration

Governance is a team effort. Too often, data scientists, legal teams, compliance officers, and executives all work from different playbooks. Collibra helps close that gap by providing a shared platform where everyone speaks the same governance language.

With role-based dashboards and a centralized glossary, teams can align on definitions, responsibilities, and standards:

• Executives get a high-level view of business value and risk 
• Compliance can monitor legal compliance 
• Data science teams know exactly what’s expected of them. 

All this removes ambiguity and makes governance a collaborative process rather than a blocker, which it so often becomes.

Quick actions with Collibra:

• Build a business glossary in Collibra to unify terminology.
• Define team member roles with clear permissions and dashboards.
• Create governance workflows to streamline approvals across teams.
• Use dashboards to share insights and keep everyone aligned on outcomes.

Step 5: Monitor compliance in real time

Regulations like the EU AI Act – though obviously necessary – demand constant vigilance from businesses dealing with enormous amounts of data. Collibra enables real-time alerts when sensitive data ends up in unapproved models or when policies are broken.

Pre-built templates simplify reporting for frameworks like GDPR, Collibra Data Privacy, and the AI Act.

Quick actions with Collibra:

• Activate real-time alerts for compliance risks.
• Use templates to generate audit reports instantly.
• Continuously monitor data access and enforce data management offerings.

Putting enterprise AI governance into practice: A financial services example from a Murdio client

Let’s give you a real-world example. One of Murdio clients, a global financial institution, recently found itself under increased regulatory scrutiny because of fragmented and inconsistent management of its AI and machine learning models. An internal audit showed that model information was scattered across disconnected systems, with inconsistent formats and incomplete documentation. 

Without a centralized “golden source” of model metadata, the organization faced significant operational and compliance risks, particularly as regulators increased their focus on AI transparency and accountability.

To close this gap, the company launched an initiative to build a centralized AI Inventory Platform – a Collibra-inspired system designed specifically for AI/ML governance. They already had a 20-person internal team in place. But the complexity of the project required specialized skills in architecture, integration, and automation.

The Client hired four of Murdio’s Collibra experts to join their team. Working side by side with internal stakeholders, Murdio helped the institution establish:

• An API-first architecture that automatically registers new models into the inventory and enables other systems to query metadata seamlessly.
• Integrations with legacy tools to consolidate previously siloed model data into a unified, consistent source.
• A flexible import mechanism, modeled after Collibra’s import capabilities, allowing onboarding of historical data via CSV and JSON templates.
• A user interface, built in Angular, enabling stakeholders to browse and manage metadata more intuitively.
• Cloud-native deployment on Microsoft Azure, ensuring scalability and resilience through Azure Web Apps and Functions.
  

Although still in early adoption, the platform is already being used by multiple teams. With Murdio’s collaborative approach, the client has moved towards:

• A more transparent and consistent inventory of AI models, reducing regulatory risk.
• Stronger lifecycle management of AI/ML models, building the foundation for ongoing governance, monitoring, and reporting.
• Faster delivery against project milestones thanks to embedded technical expertise.

The institution now plans to expand the platform’s UI, onboard additional systems, and connect the inventory directly to compliance reporting and governance workflows, moving closer to a fully operationalized AI governance framework.

Read the full case study.

The bottom line  

We can all agree that operationalizing AI governance is more than satisfying regulators. It really creates the foundation for the kind of AI that people can truly rely on – inside and outside the organization. And with Collibra, governance shifts from being just a set of abstract policies into something tangible: a living framework that gives clarity to teams, confidence to executives, and reassurance to customers.

We’ve already seen this vision take shape, and the opportunity is open to every organization. When you operationalize AI governance with Collibra, you’re building the trust and resilience that are necessary to effectively work with AI and benefit from it, minimizing the risks that are always there.

And if you want to work with experienced experts in Collibra AI Governance to help you achieve the above, reach out, and let’s chat!

Frequently asked questions about Collibra AI Governance

What does it mean to operationalize AI governance?

Operationalizing AI governance means turning policies and principles into everyday practice. Instead of governance being just a document, it becomes embedded in workflows, tools, and processes, making sure your AI models are transparent, compliant, and reliable.

How does Collibra support AI governance?

Collibra provides a unified governance platform for data and AI  that serves as the “translator” between policy and practice. It centralizes AI models and datasets in one place, automates documentation and monitoring, connects with ML platforms, and enables cross-team collaboration through shared dashboards and workflows.`

How quickly can an organization see value from operationalizing AI governance with Collibra?

A lot depends on the starting point and how you implement Collibra. Many organizations see benefits early on, such as reduced time spent on documentation, improved transparency for compliance, and faster onboarding of new AI models. And when you work with Collibra experts like Murdio to adjust Collibra to your specific AI governance use cases, you’ll see more value much faster, while also optimizing your investment in the tool.

Why can’t AI governance just be handled with policies?

Policies are important, but without execution they don’t change how AI is built or used. Many organizations struggle with silos, manual documentation, and a lack of visibility. Operationalization closes this gap by automating checks, standardizing documentation, and creating shared accountability.

What role does Collibra play with regulations like the EU AI Act?

Collibra helps organizations stay compliant by giving real-time visibility into model usage, tracking sensitive data, and generating audit-ready reports. With pre-built templates and automated workflows, teams can monitor compliance continuously instead of scrambling when it’s audit time.