Concept | AI governance#

AI governance is a broad topic without a universally accepted definition. Its scope can vary widely. In general, it refers to the processes, policies, and tools that ensure the responsible design and deployment of AI systems. The goal is to maximize benefits, while reducing unintended negative consequences.

As The Universal AI Platform^TM, Dataiku provides a unified, secure, and scalable environment to support an organization’s AI governance framework.

The need for AI governance#

To successfully scale their AI journeys, many organizations recognize the need for a dedicated layer of governance as part of their technology stack. This is particularly true in the face of evolving regulatory landscapes.

Such organizations acknowledge that scaling AI requires the right combination of the following elements:

Element	Reasoning
Democratization	Scaling AI requires engaging people with diverse skills and expertise at every stage of development and use.
Acceleration	Rapid technological change creates pressure to deliver faster return on investment from AI.
Trust	Maintaining confidence in AI is hard-earned, yet easily lost.

Understanding AI governance#

The term governance can refer to many organizational needs, from data governance to user management. In the same way, AI governance can cover a variety of perspectives depending on the context and stakeholders involved.

Different stakeholders may emphasize different aspects, ranging from compliance and risk management to fairness, explainability, or operational efficiency. This diversity highlights both the breadth and the evolving nature of AI governance.

Definitions also shift quickly over time as new technologies and new organizational needs emerge. Some perspectives focus on the why (ethical, business, or regulatory reasons), while others emphasize the how (practical tools like explainability, monitoring, or compliance).

Important

Because AI governance has no single definition, organizations should first clarify their priorities (compliance, ethics, risk management, value monitoring, etc.) to apply any governance framework effectively.

Dataiku’s approach to AI governance#

AI governance in Dataiku is a platform-wide value proposition. It’s not confined to a single feature or tool. The platform offers a range of capabilities to help organizations implement governance across the AI lifecycle.

The following table highlights some common AI governance needs and how the Dataiku platform addresses them. It’s not an exhaustive list, as many other capabilities across the platform also contribute to AI governance.

Capability	Description	Example features
Explainability	Understand why a model provides a specific result.	Feature importance, partial dependence plots, and individual prediction explanations.
Bias & fairness analysis	Assess whether a model is fair and equitable.	Subpopulation analysis and model fairness reports.
Data governance	Responsibly share data across the organization.	Data catalog, Data Lineage, Data Quality views, and fine-grained access permissions.
Auditability	Track who has done what and when.	Audit trails and logs available across all nodes.
Risk & compliance	Enforce standard processes and best practices for compliance and risk mitigation.	Customizable documentation templates and workflows, risk-value matrices to monitor AI projects.
GenAI & agent governance	Ensure responsible and cost-efficient use of generative AI models.	GenAI guardrails, PII detection, usage quotas, cost tracking, content moderation.
Operationalization	Manage the end-to-end lifecycle of AI models.	MLOps capabilities across Design, Automation, and API nodes, forming the cornerstone of effective AI governance.
Documentation	Automatically create project and model reports.	Flow document generator and model document generator.

Next steps#

Within this platform-wide framework, the Dataiku Govern node plays a key role. To learn more, see Concept | The Govern node’s role in the Dataiku platform.