Microsoft Purview vs. Collibra: Which is Better for AI Governance?

AI is showing up everywhere in the business now. One team may be using Copilot, another may be testing ChatGPT, and somewhere else, people could already be building AI agents. 

At first, that may not seem like a big problem. But once these tools start using company data, things can get complicated pretty quickly.

Who has access to what? Is sensitive information getting into AI prompts? Who is keeping track of all the AI models, agents, and use cases? And more importantly, who is responsible if something goes wrong?

This is why the Microsoft Purview vs. Collibra for AI governance comparison is worth looking at.

Both the platforms approach AI governance from different sides. For Microsoft Purview, protecting company data and managing crises around AI usage comes first. On the other hand, Collibra gives companies a way to keep track of AI models, agents, use cases, assessments, and approvals.

So, which AI governance platform makes more sense? Well, it really depends on what you’re trying to govern and the problem you’re trying to solve.

Let’s look at both and find out where each one fits.

What is Microsoft Purview?

When companies have data spread across different applications and systems, keeping track of it can become difficult. And once AI starts using that same data, there is another layer to worry about.

That is basically where Microsoft Purview comes in. It helps companies get a better view of their data and manage things like sensitive information, data risks, governance, and compliance from the same broader platform.

Now, AI has become another part of that picture. Microsoft Purview provides security and compliance protections for Microsoft Copilots and agents, enterprise AI apps, and other supported generative AI tools. This helps companies look at how AI is interacting with their business data and put controls around that usage. 

So, when we talk about Microsoft Purview for AI governance, the focus is largely on the data behind AI. Companies can basically look at what data AI is using, where there may be risks, and what security or compliance controls need to be applied.

What is Collibra

Collibra is a data and AI governance platform. For AI governance, it provides a central place where companies can keep track of their AI use cases, models, and agents, along with the governance work around them. 

Its AI governance offering has now evolved into AI Command Center, which brings the AI registry and governance view into one place. 

Companies can use it to document AI ownership, business value, and risk, and include assessments and approvals as AI moves through its lifecycle.

Quick Comparison: Microsoft Purview vs. Collibra

Before getting into each area, here’s a quick look at how the two platforms compare.

Microsoft Purview vs. Collibra: Core Comparison

1. AI Inventory and Visibility

Before a company can govern AI, it first needs to know what is being used. This could include AI apps, models, agents, or different AI projects running across teams.

Microsoft Purview

With Microsoft Purview, the view starts largely around AI activity and company data. Microsoft provides security and compliance capabilities for Copilots and agents, enterprise AI apps, and certain other generative AI tools. 

The idea here is to understand where AI and sensitive business data may be coming together. Purview can provide visibility and controls around data risks across traditional applications as well as AI apps and agents. 

So, the visibility you get from Microsoft Purview is closely connected to the data and the risks around how AI is using it.

Collibra

Collibra looks at visibility a little differently. Its AI Command Center has a central registry where companies can bring their AI use cases, models, and agents together. 

That registry also keeps information such as the owner, status, lifecycle stage, and AI Trust Score with the AI asset. So, instead of having separate lists of AI projects across different teams, companies can see the registered AI assets from one place.

2. Data Governance for AI

AI is only as useful as the data it works with. If that data is difficult to find, poorly understood, or not properly managed, then bringing AI into the picture doesn’t really fix the problem.

Microsoft Purview

With Microsoft Purview, companies can discover, understand, and manage data across their data estate. Its data governance capabilities include Data Map and Unified Catalog, which help teams work with metadata, data quality, and the overall health of their data. 

This becomes important for AI because companies need some confidence in the data being used. Microsoft Purview also brings data security into the same picture, with capabilities focused on sensitive data, data risks, and accidental data exposure through generative AI. 

So, the approach here is not to look at AI governance separately from the company’s existing data governance and security work.

Collibra

With Collibra, data governance starts with giving company data more context. Teams can define business terms, assign ownership, manage policies, and work with data quality and access as part of the governance process. 

That same governance context can then sit alongside its AI governance work. Collibra says its AI approach connects AI models, agents, and use cases with the data, policies, and governance information they depend on.

3. AI Risk Management

AI risk can mean different things. Sometimes the problem is sensitive company data being exposed through AI. In other cases, the concern is whether an AI model or agent has been properly reviewed before people start using it.

Microsoft Purview

With Microsoft Purview, a lot of the risk management work starts with the data.

For example, its Data Security Posture Management capabilities include data risk assessments that can find potential data oversharing. Microsoft notes that generative AI can make existing problems more visible because it can quickly surface content that may be over-permissioned, outdated, or missing governance controls. 

Once an issue is found, Purview provides ways to address it through controls such as Data Loss Prevention and sensitivity labels. 

So, if the concern is around sensitive information getting into the wrong AI interaction, this is an area where Microsoft Purview puts a lot of attention. 

Collibra

Collibra looks at risk more from the AI asset itself. The AI Command Center brings AI use cases, models, and agents into one governed view and includes risk and compliance information as part of that picture. 

There is also an AI Trust Score. It uses governance information such as documentation, data integrity, lifecycle status, and regulatory signals to give teams a view of the trust around an AI system. 

The score changes as information about an AI asset changes, including when assessments are completed or the asset moves through its lifecycle.

4. Policies, Compliance, and Regulatory Readiness

Sooner or later, someone is going to ask questions about an AI use case. Was the risk checked? Who reviewed it? Which rules apply? And is there anything that still needs to be fixed?

This is where policies and compliance become a real part of AI governance.

Microsoft Purview

With Microsoft Purview, companies can use Compliance Manager to work through regulatory requirements. It comes with assessments for common regulations and industry standards, and companies can build their own assessments as well. 

What I find useful here is that it doesn’t stop with an assessment. There are improvement actions that show what still needs attention, along with a compliance score that reflects progress. 

AI also sits within the larger compliance picture. Microsoft Purview includes compliance controls for AI interactions around retention, regulatory requirements, auditing, and investigations. 

So, companies already using Microsoft Purview for compliance don’t necessarily have to treat AI as a completely separate exercise.

Collibra

Collibra comes at this from a slightly different direction. Here, companies can run assessments around the AI use case itself. For example, teams can document legal and ethical concerns, business risks, safeguards, and details about the data and models being used. 

There are also ready-made assessments for the EU AI Act and the NIST AI Risk Management Framework. 

That means the compliance work can stay connected with the AI use case being reviewed, rather than becoming a separate piece of documentation somewhere else.

5. Governing AI Agents

AI agents make governance a little more complicated. An agent may have access to company data, connect with other systems, and take actions. So, companies need some visibility into what these agents are doing and what information they are working with.

Microsoft Purview

With Microsoft Purview, the focus is again closely tied to data security and compliance.

For supported AI agents, Purview provides capabilities around their AI interactions. Depending on the agent, these can include data classification, sensitivity labels, data loss prevention, and Insider Risk Management. 

Companies can also use Data Security Posture Management to see how supported agents and their parent AI applications are being used across the organization. 

So, if an AI agent is working with company information, Purview gives security and compliance teams a way to bring those interactions into their existing data protection work.

Collibra

Collibra puts more attention on the agent itself. An AI agent can sit in AI Command Center alongside AI models and use cases. Companies can track these assets across their lifecycle and connect them with the data, policies, and use cases they depend on. 

Collibra also describes more active controls for agents. Its AI Command Center includes continuous trust signals, agent contracts for setting rules around agent behavior, and guardian agents that can supervise behavior and intervene when defined boundaries are crossed.

6. Integration With Your Existing Technology Environment

Most companies aren’t starting their AI governance program with a blank slate. They already have cloud platforms, data tools, security systems, and AI projects in place. So, whichever governance platform they choose has to work with that reality.

Microsoft Purview

Microsoft Purview naturally makes sense to look at when Microsoft is already a big part of the environment.

But Purview isn’t only about Microsoft data. Its Data Map can scan assets and multicloud sources for metadata, while its Unified Catalog brings information from different catalogs and data sources together. 

On the AI side, Microsoft also positions Purview around protecting data across Copilot experiences and agents, enterprise AI apps, and other AI apps. 

So, even in a Microsoft-heavy company, the conversation doesn’t necessarily stop at Microsoft tools. The bigger question is where the company’s data and AI are actually sitting.

Collibra

Collibra feels a little different here because it is designed to bring AI information from different environments into its governance layer.

For example, Collibra currently documents AI model integrations with AWS Bedrock, AWS SageMaker, Azure AI Foundry, Azure ML, Databricks AI, Gemini Enterprise Agent Platform, OpenAI, and others. 

The metadata from those AI models can come into Collibra as assets. From there, they can be connected with the AI use cases being governed. 

That can matter when different teams are building AI in different places and the company still wants one governance view.

7. Governance Workflow and Human Oversight

Some things still need a person to make the call. Maybe someone wants access to data. Maybe an AI use case needs to be reviewed. The question is where those checks happen.

Microsoft Purview

In Microsoft Purview, Unified Catalog has workflows for certain data governance tasks. Think about someone requesting access to a data product. That request may need approval before access is given. 

The same idea applies when a data product or glossary term is being published. The approval process can be set up around the people who need to review it. 

Collibra

Collibra takes this closer to the AI itself. An AI model or agent can start as a candidate, go into review, and then be accepted or rejected. AI use cases also have lifecycle stages of their own. Assessments can happen along the way too. There can be an owner for the assessment, and sign-offs can be included at certain points in the lifecycle.

Microsoft Purview vs. Collibra: Which One Should You Choose?

After looking at both platforms, I don’t think this comes down to one being better at AI governance than the other. They start from different places.

The easier way to choose is to look at the problem your company is actually trying to solve.

Microsoft Purview may make more sense when:

• Microsoft already plays a big role in your AI environment. Purview supports data security and compliance protections across Microsoft Copilot experiences and agents, as well as supported enterprise AI applications. 
• You’re worried about sensitive company data being used with AI. Data protection, data risk, and preventing sensitive information from being exposed are central parts of Purview’s approach. 
• Your compliance work already happens in the Microsoft environment. Purview brings together areas such as compliance management, auditing, data lifecycle management, and AI-related compliance controls.
• Copilots and AI agents are becoming part of everyday work. Purview provides security and compliance capabilities for several Microsoft agents, including Microsoft 365 Copilot, Copilot Studio, Security Copilot, and Microsoft Foundry agents. 

In other words, if the conversation inside your company sounds like,“We’re using more AI, but how do we keep our data protected?”, Microsoft Purview deserves a closer look.

Collibra may make more sense when:

• You’re struggling to keep track of all the AI happening across the company. Collibra AI Command Center brings AI use cases, models, and agents into a central registry. 
• Different AI projects need one governance view. The registry keeps governance information such as ownership, status, lifecycle stage, and AI Trust Score with registered AI assets. 
• You want governance to follow the AI itself. Collibra tracks AI use cases, models, and agents across their lifecycle and connects them with the data, policies, and use cases they depend on. 
• Your AI work is spread across different technology environments. Collibra describes its registry as a governance layer for AI assets built across heterogeneous environments. 

So, if the question you’re hearing is more like, “Do we even know which AI models and agents we have, who owns them, and where they are in the governance process?”, Collibra may be closer to the problem you’re trying to solve.

Conclusion

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Frequently Asked Questions

1. What is the difference between Microsoft Purview and Collibra?

Microsoft Purview focuses more on protecting and governing the data around AI. Collibra also helps companies keep track of the AI models, agents, and use cases themselves.

2. Can Microsoft Purview be used for AI governance?

Yes. Microsoft Purview provides data security and compliance capabilities for Microsoft Copilots and agents, supported enterprise AI applications, and other generative AI applications. Its capabilities are particularly relevant when AI governance involves sensitive data, data risks, compliance, and AI interactions. 

3. What is Collibra AI Governance?

Collibra AI Governance is now part of the newer AI Command Center experience. This is where companies can keep their AI use cases, models, and agents in one registry and see things like ownership, status, and AI Trust Score.

4. Which is better for AI agent governance, Microsoft Purview or Collibra?

If the concern is the data an AI agent works with and the security or compliance around those interactions, Microsoft Purview has capabilities designed for that. If you need to register the agent, keep track of it alongside models and use cases, and follow its governance over time, that’s closer to Collibra’s approach.

5. Can Microsoft Purview govern third-party AI applications?

Microsoft Purview isn’t limited to Microsoft’s own AI. Microsoft documents security and compliance coverage for supported enterprise AI apps as well as other AI applications, although the exact capabilities vary depending on the application. 

6. Is Collibra suitable for AI governance across different technology environments?

Yes. Collibra isn’t built around just one AI environment. Its AI governance approach is designed to give companies a common view of AI assets that may be created across different platforms.

7. Can a company use both Microsoft Purview and Collibra?

Yes, they can address different governance needs. Microsoft Purview can cover the data security and compliance side, while Collibra can provide a central view of AI models, agents, and use cases.

8. What should companies look for in an AI governance platform?

Don’t start by counting features. Start with the problem you’re having. If you’re asking,“What company data is AI getting into, and how do we protect it?”, Microsoft Purview is worth looking at closely. If the question is more like,“What AI models, agents, and use cases do we actually have, and who owns them?”, Collibra’s central AI registry is directly relevant.