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Generative AI governance tools

These tools help organizations govern generative AI applications, approved-use workflows, AI gateways, policy enforcement, runtime oversight, and evidence for fast-moving GenAI adoption.

GenAI governance is not one layer

Most buyers need both management workflows and technical controls: use-case approval, provider review, prompt and data policies, gateway enforcement, monitoring, incident handling, and reporting.

Where GPAI fits

For EU-facing programs, general-purpose AI obligations increase the need to document model providers, downstream applications, transparency responsibilities, risk controls, and change management.

Credo AI

Strong fit for enterprise GenAI governance workflows, policy controls, approvals, and audit-ready evidence.

IBM watsonx.governance

Enterprise fit for lifecycle governance, risk, compliance, and monitoring across generative AI and broader AI portfolios.

OneTrust

Good fit when GenAI governance should connect to privacy, risk, third-party, and broader trust workflows.

CalypsoAI

Strong fit where GenAI governance requires runtime controls, secure adoption workflows, and policy enforcement.

Fiddler AI

Good fit for monitoring, evaluations, explainability, and observability around production AI behavior.

Databricks Unity AI Gateway

Strong fit for teams using Databricks that want AI gateway governance close to data, models, and application development.

Zenity

Useful when GenAI governance overlaps with low-code, no-code, and agentic application discovery and control.

ModelOp

Good fit where GenAI systems need inventory, lifecycle controls, approvals, and governance reporting across heterogeneous environments.

Editorial takeaway

For GenAI, pair governance workflow with technical enforcement. A policy document alone will not control tool sprawl, sensitive data use, runtime behavior, or unapproved applications.