7 Top AI Governance Tools Compared | A Complete Roundup for 2026

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by Emily Winks, Data governance expert at Atlan.Last Updated on: December 05th, 2025 | 17 min read

Quick Answer: What are AI governance tools?

An AI governance tool helps govern AI systems by ensuring all aspects of AI use are monitored and controlled in accordance with the legal, ethical, and compliance frameworks that apply to the organization. Examples include Atlan, Holistic AI, IBM Watsonx.governance, Credo AI and OneTrust AI Governance.
Benefits: With the right AI governance tool, you can

  • Reduce bias
  • Mitigate the risk of data misuse
  • Enforce explainability
  • Ensure trust and transparency across your data and AI estate

Below: List of top AI data governance tools, what each data governance tool does well, where it falls short, and the types of organizations each tool best serves.

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What are the key functions of AI governance tools?

AI governance tools offer a wide array of features, including compliance and reporting, data governance, human oversight, and end-to-end explainability, among others. These features ensure greater transparency, accountability, and trust in the AI systems an organization uses.

Let’s take a closer look at the key features.

1. AI model registry and usage catalog


AI model registry and usage cataloging ensures that all draft, published, and used AI models are tracked for changes, approvals, among other things.

This is, essentially, a data catalog tool specialized for AI models, which is foundational for enabling AI governance.

2. AI model monitoring and explainability


AI model monitoring allows an organization to trace back the set of decisions that the AI model took for a particular process.

Think lineage, but for decisions taken by an AI model. Also included in this feature is “what if” analyses and visualizations that help explain model decisions.

3. Policy enforcement and regulatory compliance


AI models are required to comply with any regulatory legal frameworks or directives such as the EU AI Act. That’s why policy definition and enforcement must be integrated into the end-to-end AI lifecycle rather than operating in isolation.

4. Data governance and fine-grained access control


Data governance and fine-grained access control are crucial to make a lot of the other features work properly.

Sensitive data classification, tagging, and protection using row-based, attribute-based, and policy-based access control, among other approaches, adds a strong layer of security to prevent unauthorized access and misuse.

5. Issue management and remediation


Issue management and remediation is needed so that issues can be surfaced and fixed by following due process defined by the organization and the regulatory authorities.

Some of these specifications might require your organization to produce the logs for remediation to ensure the integrity of the process.

6. Transparency and accountability


Transparency and accountability are two pillars for building trust of customers in the product or service your organization is offering.

Providing customers with information about when and how AI is used through documentation, model cards, and other reports and documents goes a long way toward building trust.

Different AI governance tools support these features to various extents. You need to find the one that provides the best coverage and suits your organization’s needs.

Before taking a look at how these features manifest in popular AI governance tools, let’s review the top AI governance tools.

The top commercial AI governance tools available today are:

  1. Atlan: End-to-end centralized AI asset metadata management using a unified control plane for metadata; strong data and AI governance features, for automatic discovery, classification, monitoring, policy enforcement, and compliance readiness.
  2. IBM watsonx.governance: Lifecycle, risk, security, and compliance governance and management built on the watsonx.data open data lakehouse.
  3. OneTrust AI Governance: AI asset inventory with a focus on operationalizing frameworks and standards, while also ensuring the simplification of compliance using automated discovery, asset mapping, and regulatory framework tracking.
  4. Credo AI: End-to-end governance across the AI lifecycle with reports and dashboards to track AI risk, compliance, and the overall value of the AI assets.
  5. Fiddler AI: AI observability and governance platform offering explainability, drift detection, bias monitoring, and performance insights across ML and LLM systems.
  6. Holistic AI: AI governance platform with mapping, inspection, and management of AI in your organization supported by AI asset discovery, AI inventory, AI system audits, regulatory, and operational reporting.
  7. Collibra: Platform for streamlining the AI use case lifecycle, ensuring AI traceability, tracking, and governing AI models in a model registry.
  8. Dataiku: Enterprise AI and analytics platform with governance layers for model documentation, approval workflows, risk scoring, and auditability.
  9. DataRobot AI Governance: Focuses on real-time AI governance and compliance with a central hub for all AI assets, related policies, compliance adherence tests, and alerts.

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Are there any open-source alternatives for AI governance?


Meanwhile, if you’re looking for open-source alternatives for AI governance, there aren’t many in the market. The only open-source AI governance product that exists is VerifyWise.

If you still want to go the free and open-source route, you can build your own AI governance tool on top of low-level tools like MLFlow, Kubeflow, and others.

1. Atlan

Atlan is an enterprise-grade AI governance and cataloging tool that spans across the entire data and AI ecosystem of an organization.

It is built on the foundation of a unified metadata control plane and a metadata lakehouse to leverage all types of metadata: technical, business, and operational for enabling data and AI governance across the board.

Recognition for Atlan’s future-forward governance capabilities

  1. Visionary in Gartner MQ for Data & Analytics Governance Platforms, 2025
  2. Leader in Forrester Wave™: Data Governance Solutions, Q3 2025
  3. Snowflake Partner of the Year (2025) - Data Governance

Key AI governance features

Top AI governance features that make Atlan, that best AI governance tool in the market:

Top customers: General Motors, NASDAQ, Yape, Elastic, Ralph Lauren, Unilever, NHS.

Used by: Modern enterprises with more than $10T in enterprise value in IT, financial services, CPG, retail, and more.

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2. IBM watsonx.governance

IBM watsonx.governance is an enterprise solution in IBM’s watsonx suite of products. It specializes in monitoring and compliance for agentic AI. It can be deployed on-premises or in the cloud.

Top AI governance features

  1. Automated end-to-end governance for agentic AI built on top of LLMs irrespective of the vendor.
  2. Automated logging, monitoring, and audit trails to ensure reports, dashboards, and factsheets for both internal and external AI governance and performance assessments.
  3. Compliance accelerator templates for quickly implementing policies around specific regulations like the EU AI Act.

What’s missing

  1. IBM watsonx.governance has limited capabilities in linking multiple objects or models together, which hampers explainability and observability of model decisions.
  2. Users on TrustRadius have noted that integrating with tools and systems outside IBM is either very challenging and, in many cases, currently not possible.
  3. IBM watsonx.governance can have a very steep learning curve, preventing teams from adopting them quickly.

Best suited for: Large enterprises that are heavily invested in the IBM ecosystem and suite of products.

3. OneTrust AI governance

OneTrust AI Governance is an enterprise solution that is a part of the OneTrust Platform. The AI Governance product is designed for AI-based risk identification and mitigation, especially to handle governance, risk, and compliance workflows.

Top AI governance features

  1. Automated linking of AI assets with relevant AI compliance and regulatory actions and activities.
  2. Automation of regulatory compliance in relation to specific laws and frameworks based on different geographical regions and industries.
  3. An ability to integrate with a wide variety of AI and data platforms and leverage them for automated data discovery, governance, compliance, and policy enforcement across the board.

What’s missing

  1. The product is not easy to learn and use for newly onboarded users. It has a steep learning curve, which has led to very low, slow adoption.
  2. As the focus of the platform is primarily GRC (Governance, Risk, and Compliance) workflows, other aspects of AI governance get left out, such as observability and explainability, among other things.
  3. Too many tasks, options, and frameworks available to customize, which makes the tool unreasonably complicated and resource-heavy). There’s also no way to get started quickly and easily.

Best suited for: Large enterprises with a global footprint that have a large enough workforce to tackle complex GRC workflows in the OneTrust Platform, especially when the auditing and compliance requirements are complex.

4. Credo AI

Credo AI is an enterprise AI governance platform that provides end-to-end lifecycle governance, with a focus on observability, transparency, and risk mitigation.

It helps you manage agentic AI and model-specific risks while automating compliance with specific laws and regulations.

Top AI governance features

  1. AI asset registry: Besides AI asset discovery, Credo AI also leverages the metadata for asset prioritization based on risk, compliance, impact, etc.
  2. Automated policy templates (policy packs): Like many other tools, Credo AI also supports template-based policy enforcement for specific laws, regulations, frameworks, and directives. You can also create your own policy packs if you don’t find one for your use case.
  3. Strong integration with third-party AI vendors: Integrates with all the major AI and data platforms, such as Snowflake and Databricks to leverage their data cataloging and governance features.

What’s missing

  1. For small to medium-sized organizations, the setup is challenging and adoption is low, making it tougher to justify the costs of implementation and maintenance.
  2. While some basic risk and compliance reports are available, they are not customizable enough for an organization to derive value.

Best suited for: Credo AI is designed to handle large and complex AI governance scenarios, especially in industries that require extensive safety and compliance regulations.

5. Holistic AI

Holistic AI is an AI Governance Platform focused on AI compliance and security for the end-to-end AI lifecycle.

It uses multiple factors to assess and mitigate AI risks related to bias, privacy, and explainability, among others. It specifically focuses on PII safety in LLMs.

Top AI governance features

  1. Risk classification based on specific regulations: Holistic AI does that specifically for EU AI Act, as it uses a dashboard to highlight high (Red), medium (Amber), and low (Green) risks in regulatory compliance.
  2. Extensive LLM auditing: Checks for bias induction, sensitive data leakage, hallucinations and toxicity creep, among other things.
  3. Mitigating Shadow AI: Automatically detects shadow AI after every deployment, including checking for AI model use in scripts and codebases.

What’s missing

  1. No data catalog. One of the key items that makes AI governance work, a comprehensive data catalog, has less of a focus in Holistic AI than other AI governance tools. Holistic AI is more focused on risk and security compliance.
  2. Gaps in MLOps monitoring. Despite the focus on security and risk, there’s a lack of deep, granular, and technical MLOps logging and observability, which might, in some cases, require integration with other third-party tools.

6. DataRobot

DataRobot AI Governance is a real-time AI governance and compliance platform with AI asset discovery and cataloging, with integrated compliance policies, rules, and alerts.

Top AI governance features

  1. Centralized AI asset registry: Employs a single, central asset inventory for all AI use cases. The asset inventory includes essential features for monitoring, observability, alerting, and notifications.
  2. Automated compliance with policy-driven governance: Allows you to enforce multi-level approvals for access controls. It also includes testing and monitoring capabilities across the AI lifecycle.
  3. AI model observability: Logging, monitoring, and alerting capabilities for data quality, policy violations, model drift, etc.

What’s missing

  1. Many users of DataRobot say that while it is a good AI governance tool, it lacks customization capabilities to support the complex regulatory and compliance use cases.
  2. Some users also noted that the user experience of DataRobot’s AI governance tools is not intuitive enough for users to just start using it, which is typically why such projects get shelved even after an initial implementation.
  3. For some of the core AI governance features, DataRobot may need third-party AI models and other AI and ML tools to support end-to-end governance across the AI lifecycle.

Best suited for: Medium to large organizations that are familiar with DataRobot, already have it in place, and are willing to adopt other third-party tools to cover data cataloging and features that DataRobot doesn’t natively support.

7. Collibra

Collibra is a data intelligence platform with data cataloging and governance capabilities that can also be leveraged to manage AI assets. It provides observability, traceability, and explainability for AI use cases.

Top AI governance features

  1. Centralized governance: Applies consistent governance policies across both traditional data assets and AI models, eliminating silos between data governance and AI governance teams.
  2. Model catalog and registry: Maintains an inventory of AI models with metadata, relationships, ownership, and usage information accessible to both technical and business users.
  3. Integrated AI and data governance: Leverages Collibra’s core data governance platform to provide visibility and control over both data assets and AI models throughout their lifecycles.

What’s missing

  1. Users have consistently noted that Collibra has a steep learning curve, owing to complex workflows and configurations, due to which their organizations face adoption challenges in the short to medium term.
  2. Collibra does not have native support for managing AI model lifecycle. However, it can integrate with third-party MLOps and AIOps tools to provide this feature.

Best suited for: Large enterprises that need data governance features such as lineage and cataloging, with advanced requirements for AI monitoring, observability, security, safety, and compliance.

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Ready to choose the best AI governance tool for your organization?

AI governance is a nascent concept. The needs and requirements around AI governance are changing rapidly as models evolve at an unprecedented rate.

In such a scenario, you need to look at an AI governance tool that is slightly ahead of the curve compared to the rest of the players and has a well-thought-out product roadmap.

Look at the key features your organization needs to maximize the benefits of AI without compromising any of the safety and security controls, while also abiding by any laws, regulations, and directives that the organization is obliged to follow.

If you make the right decision with the AI governance tool, you’ll make the lives of your team members much easier, while also gaining their trust at the same time.

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FAQs about AI governance tools

1. What is AI governance?


AI governance is the framework of policies, processes, and oversight mechanisms that ensure AI systems are trustworthy, compliant, explainable, and aligned with organizational and regulatory expectations.

It covers the full AI lifecycle—from data sourcing and model development to deployment, monitoring, and retirement.

AI governance provides the guardrails needed to manage risk, prevent bias, track lineage and provenance, enforce accountability, and ensure responsible use of AI across the enterprise.

2. What does an AI governance tool do?


AI governance tools help organizations manage, monitor, and control the legal, ethical, and operational performance of their AI systems.

They provide visibility into how AI models are built, deployed, and used, and where they may introduce risk. This includes detecting bias, data privacy issues, intellectual property exposure, model drift, hallucinations, security vulnerabilities, and other forms of AI misuse or unintended behavior.

3. Why do enterprises need AI governance tools now?


The rise of LLMs, agentic AI, and high-stakes automation has introduced new risks, such as hallucinations, data leakage, bias, and opaque model decision-making.

Regulations like the EU AI Act, FTC guidelines, and industry-specific rules require traceability and documentation. AI governance tools operationalize these requirements at scale.

4. What features should I look for in an AI governance tool?


Look for capabilities such as:

  • AI model registry, usage and metadata management
  • Model lineage and versioning
  • Drift, bias, and performance monitoring
  • Automated policy setting and enforcement
  • Automated documentation for audits
  • Data governance with fine-grained access control and compliance reporting
  • Explainability driven by data quality monitoring and end-to-end data estate transparency
  • Integrations with modern tools, such as MLOps, data catalogs, and model registries
  • LLM/GenAI-specific controls (prompt logs, output evaluation, guardrails)

5. How do AI governance tools support regulatory compliance?


AI governance tools automate compliance efforts by:

  • Maintaining audit trails of model training, datasets, parameters, and runtime behavior.

  • Generating risk assessments mapped to frameworks like the EU AI Act, NIST AI RMF, ISO 42001, and industry rules (banking, healthcare, etc.).

  • Enforcing access controls and data policies around training data and model outputs.

    This reduces manual documentation from months to minutes and ensures readiness for internal or external audits.

6. What is the difference between AI governance and MLOps?


MLOps focuses on building, deploying, and monitoring models for performance and reliability.

AI governance ensures models are safe, compliant, ethical, explainable, and properly documented.

AI governance sits above MLOps to provide oversight and traceability across the entire AI lifecycle.

7. Can I choose an open-source AI governance tool for my enterprise?


Open-source options often lack enterprise-ready features (e.g., business workflows, policy automation), so they require significant internal investment.

You can consider choosing an open source tool if you:

  • Have a large engineering team to design customizations for your tech and data stack, and maintain integrations.
  • Are experimenting with early-stage AI models.

8. Why should I choose a commercial AI governance tool (like Atlan)?


Commercial platforms like Atlan offer a unified metadata + AI control plane, making governance operational across business, risk, and technical teams.

Choose commercial tools like Atlan to get:

  • Fast implementation and lower maintenance burden
  • Enterprise compliance readiness
  • Prebuilt integrations across data, ML, and AI stack
  • AI/LLM governance features (prompt governance, evaluation frameworks, lineage, metadata tracking)
  • Cross-team adoption, not just engineering enablement

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