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Choose an AI marketing governance platform by testing whether it can manage your real workflows from inventory and risk assessment through approvals, evidence, and reassessment—not by counting framework logos. It should help your team track AI use, substantiate advertising claims, record consumer-disclosure decisions, control data use, and export an auditable history. A platform can organize this work; its framework mappings do not prove legal compliance or that its controls work as described.
Start with the governance workflow, not the feature list
Before vendor demos, list the AI-enabled marketing activities your organization actually uses or plans to use. Include tools and models, vendors, business owners, purposes, data categories, affected audiences, and lifecycle status. Then identify who assesses risk, approves use, reviews outputs and claims, and decides when a workflow must be reassessed.
This gives you a practical test: can a platform keep those records connected as a use case moves from discovery to approval and ongoing oversight? Ask vendors to demonstrate your workflows and export the resulting records. A polished dashboard is less useful if teams cannot trace an output to its owner, evidence, decision, and subsequent changes.
Capabilities to evaluate
Inventory and discovery
Check whether teams can record use cases, models, tools, vendors, owners, purposes, data categories, affected audiences, and status. Ask how the system finds changes or unregistered use: does it support discovery, or does it depend entirely on staff submitting forms? Wave2 describes tracking use cases, models, and vendors on its website; treat that as a vendor description to verify in a demonstration, not independent validation.
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Risk assessment and approval routing
A useful workflow should let teams document intended use and context, potential harms, likelihood or severity judgments, mitigations, accountable reviewers, and conditions for approval. It should route assessments to the people responsible for the decision and preserve exceptions or unresolved issues. The NIST AI Risk Management Framework (AI RMF) is a voluntary, use-case-agnostic resource for managing AI risk, not a certification for a product or a substitute for your own assessment.
Framework and control mapping
Do not stop at a framework name or logo. Ask to see the exact requirement or control, its source and version, why it applies to your use case, the evidence linked to it, the responsible owner, and any open gaps. Confirm how mappings are updated and whether your team can add internal policies and jurisdiction-specific requirements. NIST says AI RMF 1.0 is under revision, so version visibility matters; consult its AI RMF FAQs and AI RMF 1.0 publication record.
A mapping organizes work; it does not establish that your organization complies with a law or that a control is effective. Tailor any framework to the marketing use case, relevant jurisdictions, risk tolerance, and operating model.
Evidence, history, and export
Ask whether a record can retain source artifacts, approvals, timestamps, access history, changes, exceptions, and the rationale behind decisions. Test whether you can export complete records in a usable format outside the platform, rather than receiving a report that omits underlying evidence. Gamut AI describes lifecycle evidence and framework assessments in its documentation, while Wave2 describes evidence artifacts; validate the depth and usability of these capabilities hands-on.
Marketing claims and AI-assisted content
Test how the platform handles substantiation for performance, product, comparative, and endorsement claims. Can reviewers attach the source behind each claim, record who checked it, and preserve the approved version? Include AI-assisted text, images, audio, video, and synthetic personas in the test. The U.S. Federal Trade Commission says advertising claims must be truthful, non-deceptive, fair, and evidence-based; its Advertising and Marketing guidance also applies truth-in-advertising standards to software and services. A platform may support review and recordkeeping, but it does not replace legal or subject-matter review.
Consumer-facing AI disclosure decisions
Ask whether teams can record why AI involvement is or is not disclosed to consumers, what materiality or risk factors informed that choice, and which disclosure wording and placement were approved. The IAB AI Transparency & Disclosure Framework V2, published August 18, 2026, describes a risk-based, materiality-driven approach for consumer-facing advertising and marketing. It is industry guidance, not a substitute for applicable law; check that a platform supports the version and approach your organization has chosen.
Privacy, copyright, and data use
Map the data involved in prompts, model interactions, segmentation, personalization, and generated outputs. Examine access controls, retention and deletion, provider terms, prompt handling, output rights, and restrictions on sensitive or customer data. NIST’s Generative AI Profile recommends aligning generative AI development and use with applicable laws, including privacy, copyright, and intellectual-property law. Specific duties depend on jurisdiction, organizational role, and use case.
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Look for named owners, role-based permissions, human review, exception handling, incident escalation, version history, and oversight reporting. Establish which changes trigger reassessment—for example, a new model, changed data, a different audience, or a shift in intended use—and whether the platform can assign and track that work. NIST identifies trustworthiness considerations such as reliability, safety, security, accountability, transparency, explainability, privacy, and management of harmful bias; use these as lenses for decisions and evidence, not a checklist that automatically makes a system trustworthy.
Integration and procurement fit
Verify APIs and integrations with the systems your marketing, security, privacy, and compliance teams use. Ask about identity and access management, hosting region, subprocessors, support, implementation effort, data export, retention and deletion, and contract terms. Obtain current written pricing and terms and check references directly: public descriptions alone do not establish comparative performance, customer outcomes, or implementation costs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test vendors with representative marketing workflows
Use the same scenarios and scoring criteria for each shortlisted platform. Two or three workflows are usually enough to expose whether the product supports your actual controls:
- AI-assisted ad copy with a measurable product claim: trace the claim to substantiation, reviewer, approval, and final copy.
- Generated campaign imagery or a synthetic spokesperson: record the use case, risk decision, output review, and consumer-disclosure rationale.
- AI-supported segmentation or personalization using customer data: inspect data flows, access, provider terms, retention, and reassessment triggers.
For each scenario, ask the vendor to show how the record is created, who owns it, how risk is assessed, where evidence is attached, how approval and disclosure decisions are captured, what happens when the model or data changes, and how the complete history is exported. Score the same tasks across vendors and record missing controls as gaps; a framework logo does not answer these questions.
How to interpret the product landscape
Gamut AI describes a lifecycle spanning discovery, assessment, classification, governance, evidence, audit, reporting, and improvement. Wave2 describes a workspace for AI strategy, use cases, compliance, vendors, and ROI, including framework controls, inventories, and evidence artifacts. Saidot describes framework-mapped controls and guided workflows on its website. These are vendor-authored descriptions, not a comparative evaluation or proof of independent effectiveness. Demonstrate the specific workflows, integrations, security controls, and export you need before drawing conclusions.
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