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Buyer checklist

AI governance RFP checklist

Use this checklist to structure an AI governance RFP around the workflows that matter: inventory, policy, risk, controls, evidence, monitoring, and reporting.

Inventory and ownership

Ask how the vendor captures AI use cases, models, third-party systems, owners, business purpose, risk tier, data categories, affected users, deployment state, and approval history.

Policy, risk, and assessments

Require support for internal policies, EU AI Act, NIST AI RMF, ISO 42001, U.S. employment and consumer-protection expectations, impact assessments, exceptions, and human oversight documentation.

Controls, evidence, and reporting

Ask how controls map to frameworks, how evidence is collected and reused, how issues are remediated, what dashboards exist, and what exports support legal, audit, board, or regulator review.

Implementation fit

Score integrations, workflow configurability, access control, data residency, services support, pricing model, implementation timeline, and whether the platform can govern both traditional ML and generative AI.

Credo AI

Broad-fit enterprise option to include when the RFP centers on governance workflows, policy enforcement, artifacts, and reporting.

IBM watsonx.governance

Broad-fit enterprise option when lifecycle governance, compliance management, monitoring, and large-estate integration are key scoring areas.

OneTrust

Broad-fit option for buyers that want AI governance connected to privacy, third-party risk, compliance, and trust operations.

Trustible

Useful benchmark for operational inventory, assessments, controls, and evidence workflows.

ModelOp

Useful benchmark when model inventory, lifecycle controls, validation, and regulator-grade reporting are central to the RFP.

AuditBoard

Useful benchmark when audit, risk, controls, evidence requests, and issue management are major buying criteria.

Modulos

Useful benchmark when framework mapping, evidence reuse, and audit readiness are central evaluation criteria.

Editorial takeaway

A strong AI governance RFP should test operating fit, not demo polish. Ask vendors to walk through one real use case from intake through approval, monitoring, incident handling, and evidence export.