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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteChoose an AI marketing governance platform by testing whether it can manage your real workflows from inventory through risk review, approval, evidence retention, and reassessment—not by counting framework logos. Start with AI use cases, models, vendors, and accountable owners; then test how the platform handles marketing claims, consumer disclosures, data use, and changes to tools or workflows. A platform can organize those decisions, but its presence or framework mappings do not prove legal compliance or that its controls work.
Start with the work the platform must govern
Before comparing products, list the marketing workflows where AI is already used or planned. Include the tool or model, vendor, business purpose, owner, data involved, audience affected, and lifecycle status. This reveals whether the platform can represent your operating reality, rather than just provide a general-purpose AI register.
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Ask whether discovery depends on employees submitting records or can also help identify changes and unregistered use. Vendors describe different inventory capabilities: Wave2 describes tracking use cases, models, and vendors, while Gamut AI documentation describes a lifecycle that includes discovery. These are vendor descriptions, not independent validation; demonstrate the workflows and verify what the software actually detects.
Evaluate the core governance workflow
Risk assessment and routing
For each use case, the platform should let teams record intended use, context, potential harms, likelihood or severity judgments, mitigations, reviewers, and approval conditions. Check whether routing changes with risk, whether a human decision is recorded, and whether exceptions have an owner and expiry or review point.
#1 Best Overall
The NIST AI Risk Management Framework (AI RMF) is voluntary and use-case agnostic. Use it as a resource for structuring risk work, not as a product certification or a universal marketing checklist.
Control and framework mapping
A useful mapping identifies the exact requirement or control, its source and version, why it applies, who owns it, what evidence supports it, and what remains open. Ask how framework updates are handled and whether your team can inspect and edit applicability decisions. NIST says AI RMF 1.0 is under revision, so version visibility and update practices matter (NIST AI RMF FAQs; NIST AI RMF 1.0 publication record).
NIST identifies trustworthiness considerations such as reliability, safety, security, accountability, transparency, explainability, privacy, and management of harmful bias. Treat these as lenses for questions and evidence—not a checklist that by itself establishes trustworthy AI.
Evidence and auditability
Confirm that records retain the artifacts behind decisions, approvals, timestamps, access history, changes, and exceptions. Then test whether the complete record can be exported in a usable format outside the platform. Product documentation may describe evidence features, but a demonstration should establish their depth, permissions, and limits.
Ownership and change management
Look for named owners, role-based permissions, version history, incident escalation, oversight reporting, and clear triggers for reassessment. Ask what happens when a model, vendor, prompt, data source, intended use, or audience changes. A platform that captures only initial approval can leave teams with stale risk decisions.
Test marketing-specific claims, disclosures, and data use
Claims and substantiation
Marketing review should connect performance, product, comparative, and endorsement claims to the evidence supporting them. Ask whether reviewers can attach source material, document approval conditions, and revisit a claim when the underlying evidence changes. For U.S. advertising, the FTC says claims must be truthful, non-deceptive, fair, and evidence-based, and its advertising standards apply to software and services (FTC Advertising and Marketing). A governance tool should support this work; it cannot make an unsupported claim compliant.
Rank #4
AI transparency decisions
For consumer-facing advertising and marketing, ask whether teams can record why AI involvement is or is not disclosed, which materiality or risk factors informed that decision, and what wording and placement were approved. The IAB AI Transparency & Disclosure Framework V2, published August 18, 2026, describes a risk-based, materiality-driven approach. It is industry guidance, not a substitute for applicable law; verify the current version when evaluating a platform.
Privacy, copyright, and data use
Inspect how the platform documents data flows, access, retention, provider terms, prompt handling, output rights, and use of sensitive or customer data. NIST’s Generative AI Profile recommends aligning generative AI development and use with applicable law, including privacy, copyright, and intellectual-property law. The specific duties depend on jurisdiction, organizational role, and use case.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check procurement and operational fit
Governance features matter only if the platform fits your systems and operating model. Verify these items directly with each vendor:
- Available APIs and integrations with marketing, identity, and access-management systems.
- Data export, retention and deletion options, hosting region, subprocessors, and security documentation.
- Support arrangements, implementation effort, administrator workload, and contract terms.
- How permissions, exceptions, and reporting work across marketing, legal, privacy, security, and procurement teams.
Current pricing, implementation costs, contract terms, and comparative performance are not established here. Request written proposals and validate customer references directly.
Run the same demonstration across vendors
Bring two or three representative workflows, such as AI-assisted ad copy with a measurable product claim, generated campaign imagery or a synthetic spokesperson, and AI-supported segmentation or personalization using customer data. Use the same tasks and scoring weights for every shortlisted platform.
- Create the record: Show how the use case, model, vendor, purpose, data categories, audience, and accountable owner are captured.
- Assess and route risk: Record potential harms, judgments, mitigations, reviewers, and approval conditions.
- Review marketing obligations: Attach claim substantiation and document any consumer disclosure decision, including approved text and placement.
- Handle change: Change the model, data, or intended use and show whether the platform triggers review and preserves the decision history.
- Export the evidence: Produce the full record, including approvals, artifacts, exceptions, and version history, in a usable format.
Score each platform on inventory coverage, risk routing, mapping depth, evidence and export, marketing review, data controls, integrations, reassessment, and procurement fit. Record missing controls as gaps. Framework logos or vendor claims are not substitutes for demonstrating the tasks.
Compare vendors without assuming a winner
Product materials illustrate possible approaches, but do not establish a comparative winner. Gamut AI describes a lifecycle covering Discover, Assess, Classify, Govern, Evidence, Audit, Report, and Improve, along with assessment against named frameworks. Wave2 describes a workspace for AI strategy, use cases, compliance, vendors, and ROI, with framework controls, inventories, and evidence artifacts. Saidot describes framework-mapped controls and guided workflows. Treat each as vendor-stated capability and validate workflow depth, integrations, security, and export in your own demonstration.
Quick Recap
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