Most website and tag governance platforms, including Tag Inspector, have been limited in scale by their consent management platform (CMP) support. Niche players, custom CMPs, and non-standard consent options have been at the fringes with incomplete capabilities to effectively ensure user consent is consistently being honored.
This changes with Tag Inspector’s AI Enabled CMP Scanning.
Handling the consent auditing needs of any website
With AI enabled scanning, Tag Inspector is now able to reliably simulate user consent states for any CMP that is in use.
Why CMP coverage has always been a problem
The consent management market is fragmented. Enterprise organizations often run custom-built consent layers. Regional publishers use niche vendors designed to their specific needs, but aren’t prioritized on many platforms’ integration list. Even among major CMPs, consent UI varies enough that standard simulation approaches break on edge cases.
Tag governance platforms audited what they knew how to interact with, but everything else was a blind spot. Teams either accepted partial coverage or built manual workarounds for non-standard setups.
What AI-enabled scanning changes
Rather than relying on pre-built integrations for specific CMP vendors, Tag Inspector’s AI-enabled scanning uses a model-driven approach to recognize and interact with consent interfaces it has never explicitly seen before. The system identifies consent UI elements, maps them to consent states (accept, reject, granular selection), and simulates those states with enough reliability to generate audit data that reflects how tags fire under each consent condition.
This matters most in three scenarios:
All Available CMP Vendors
Tag Inspector now supports the full range of known CMP providers without requiring a dedicated integration for each one. If a vendor has a recognizable consent interface, the scanner can work with it. For organizations running less common but commercially available CMPs, usable coverage expands significantly.
Custom CMPs
Organizations with homegrown consent management infrastructure have historically been difficult to audit at scale. Custom CMPs often don’t follow standard interaction patterns. AI-enabled scanning closes this gap by learning the consent interface rather than matching it against a known template.
Non-Standard Consent Options
Not every consent interaction follows a standard accept/reject binary. Some implementations include granular purpose-level selections or region-specific consent flows that don’t fit a typical CMP mold. The AI scanning approach handles these variations without requiring manual configuration for each case.
What this means for compliance teams
If your organization runs a niche CMP, recently switched providers, or maintains a custom consent layer, you can now run the same structured consent simulation audits that were previously available only to organizations on major platforms.
This also reduces the operational overhead of maintaining CMP coverage. As CMPs update their UIs or your organization changes providers, audits continue running without gaps. Coverage does not depend on timing a provider change around platform support cycles.
For teams managing consent compliance across large property portfolios with varying CMP implementations, this means consistent audit methodology across all properties. Every property gets the same quality of consent state simulation, regardless of what is running under the hood.
Getting Started
AI-enabled CMP scanning is automatically applied within Tag Inspector’s existing scanning and audit workflow. No additional configuration is required to begin auditing sites with previously unsupported or custom CMPs. Scans will automatically apply AI-enabled interaction for consent interfaces that fall outside Tag Inspector’s standard CMP library.
If you are managing properties that have been excluded from consent simulation audits due to CMP compatibility issues, reach out to your InfoTrust team to gain access.
