Are your AI video models compliant?

Storyboard to video depends on character consistency. You define actors once and keep them recognizable across every scene. To do that, we send reference images of those actors into the video model on every generation.

Our high-quality video path was using Seedance, from ByteDance (TikTok's parent company). Seedance runs a face-detection pass on every uploaded image before it reads the prompt, and it hard-blocks any image with a recognizable human face. Actors are the core of the feature, and the model refused those inputs. This shows up everywhere. Creators hit the same wall constantly, and third-party API providers warn that "images with real human faces are blocked by ByteDance content policy." Rewriting the prompt does not help. The prompt is never the problem. The image is rejected at a separate scan stage before the prompt is read.

We could have shipped a workaround. Instead we moved the high-quality video tier off Seedance. That choice mattered more once model selection also became a compliance issue.

Model choice is becoming a compliance question

The U.S. government is tightening two related sets of rules around AI.

The first is already law. The Department of Justice Data Security Program restricts certain transactions that give China and other "countries of concern" access to bulk sensitive U.S. personal data or government-related data. It was issued under Executive Order 14117, took effect on April 8, 2025, and moved into active enforcement on July 8, 2025. China, including Hong Kong and Macau, is one of the six designated countries of concern. Covered categories include biometric identifiers, precise geolocation, personal health data, personal financial data, genomic data, and certain personal identifiers. This is not a blanket ban on sending any data outside the United States. Applicability depends on the type and volume of data, the transaction, the provider, and the provider's ownership or jurisdiction. See also the Federal Register final rule.

The second set is newer and still moving. Treasury Secretary Scott Bessent threatened sanctions against Chinese AI companies over intellectual property theft, and the White House accused Moonshot AI of distilling Anthropic's Fable model, with Entity List designations "on the table." Reporting from Tom's Hardware and MLQ describes a broader push (procurement restrictions, security advisories, and liability rules) that stops short of a formal ban but creates real friction for any U.S. business built on a Chinese model.

Not just one model, or one country

The point is not that Seedance is uniquely bad. Every model you build on comes with a policy, a jurisdiction, and an availability window you do not control. Any of those can change without notice.

• Chinese video models are the most exposed today. ByteDance's Seedance, Kuaishou's Kling, and MiniMax's Hailuo each combine aggressive, opaque content filters with rising regulatory risk.
• Moonshot AI's Kimi, billed by its maker as the largest open-weight model in the world and popular enough that Microsoft has reportedly been testing it for Copilot and Azure, is now at the center of the sanctions fight. Teams that standardized on it may need an exit plan.
• U.S. and European providers have surprises too. ElevenLabs bars government entities from using its service without prior written authorization. If you sell into the public sector, that clause can block you just as effectively as a geopolitical one.

The set of acceptable models keeps moving, for reasons that have nothing to do with your product roadmap.

What VideoGen does about it

VideoGen sits between your workflow and the underlying providers. The model router chooses which model runs each generation based on quality, availability, and eligibility.

After Seedance, we moved high-quality video generation onto Google's Omni model, with automatic fallback to other vetted, non-Chinese providers. If a model rejects a request, goes down, or becomes unsuitable, the router routes around it on that request. You do not need to rewrite the workflow. Actor references generate cleanly on the current path, and we manage model eligibility as part of operating the stack.

We continuously evaluate the models and providers available through VideoGen, including:

• Where requests are processed
• Which company operates each model
• Whether data is retained or used for training
• Which models can be used for particular customers or regions
• How to replace a restricted model without breaking the workflow

This is not a legal guarantee that your company is compliant. Compliance depends on your data, your customers, and your counsel. What we can substantiate operationally is model eligibility, data-routing controls, and fallbacks that keep your workflow running when a model becomes unsuitable.

The takeaway

If you are building AI video into a product or internal workflow, avoid hard-wiring your stack to a specific model. Inventory what your stack actually calls (including through aggregators). Ask where inputs are processed and retained. Prefer a platform that can swap models without a code change.

That is how VideoGen is built. Eligibility and routing stay an operational concern, so your product does not have to absorb every provider policy change.

Start creating with VideoGen, or talk to us about model eligibility for enterprise or regulated use.