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Yes—multiple game studios can use a shared generative AI service, but safety depends on the data each studio submits, who can access it, what the provider may do with it, and how the studios divide responsibility. A shared service is not automatically a shared workspace with adequate studio or project separation. Before use, map the data flows, verify the applicable product terms and settings, and agree on access, retention, review, and offboarding.
What “shared” means—and where the risk sits
A common service may mean one vendor account, one interface or orchestration layer, or a common model used by separate studio workspaces. Those arrangements do not necessarily provide the same privacy or access boundaries. Evaluate the actual architecture and configuration rather than assuming that a different project name or team folder prevents cross-studio access.
There are at least two relationships to govern: exchanges between participating studios, and transfers from each studio to the provider and any subprocessors. Data may also pass through logs, retrieval stores, feedback channels, or generated outputs. NIST SP 800-47 Rev. 1 provides a general framework for protecting information before, during, and after an exchange: identify the exchange, consider appropriate safeguards, and document responsibilities in agreements tailored to the organizations involved.
The practical question is not simply whether the model is “shared.” It is who can submit, retrieve, retain, export, or reuse each kind of information at every point in the service.
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Can multiple game studios safely use the same generative AI system?
They can, if the arrangement matches the sensitivity of the work and has enforceable controls. Treat this as a governance and procurement decision, not as a setting that can be settled by a single vendor promise. A suitable arrangement should establish studio- and project-level access boundaries where available, define approved uses, and specify how data and incidents are handled.
- Classify what may be submitted. Identify source code, unreleased builds, design documents, character and story material, voice or likeness data, player data, credentials, and third-party licensed assets. This is a practical inventory, not a complete legal classification.
- Map the route. Trace information from the studio user through the shared interface or orchestration layer to the model provider and subprocessors, then through logs, retrieval stores, feedback channels, and outputs.
- Test the boundary. Determine whether one studio can see another studio’s prompts, uploaded files, outputs, or usage logs. Confirm which identity, workspace, and project controls are available and how they are configured.
- Assign responsibility. Use inter-studio and vendor agreements to define permitted purposes, access, retention, deletion, incident cooperation, notification, audit evidence, and offboarding.
- Review the whole chain. Check provider terms alongside engine, marketplace, and asset-license terms. A provider’s data-use commitment does not override restrictions attached to engine code or licensed material.
Use separate identities and least privilege where available. Set an approval path for sensitive submissions, include contractors in access rules, and make removal of accounts and data part of the exit procedure. These are governance recommendations; they do not imply that every shared service offers a particular isolation feature.
Will the AI provider train on our game code or assets?
There is no single answer for every provider, product, data type, or account configuration. “Training” can also be narrower than the full data-use question: prompts and files might be retained, reviewed by people, used as feedback, or otherwise handled under terms distinct from model training. Check the agreement and the settings for the precise AI feature, then verify that the settings apply to every participating studio and service tier.
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| Example | What the cited terms or policy say | What to verify for your workflow |
|---|---|---|
| Unity AI | Unity’s AI Guiding Principles say direct training of Unity AI models on developer content is off by default. They also describe an organization allowing Unity to use “Developer Data”—including prompts, responses, interactions, code, and other content—to improve certain Unity AI models for all developers. The principles distinguish those models from generative asset models. | Check the current policy, agreement, organization setting, and exact Unity AI feature. Do not assume the default applies unchanged to every model or data category. |
| Epic / UEFN | Epic’s UEFN Supplemental Terms say Epic will not use Developer-Made Content, or license it to third parties, to train Generative AI Programs, subject to stated exceptions: localization training on corrections unless opted out, and feedback explicitly provided to the Developer Assistant. | Confirm that UEFN and these supplemental terms govern the use in question. The terms also warn that Developer-Made Content shared in the service may be visible to others and may be captured or shared in gameplay footage and screenshots outside Licensed Products. |
These examples are product-specific, not universal guarantees about either company’s services. A setting that controls model improvement does not by itself answer who can see submissions, how long logs persist, whether human review occurs, or what happens after termination. Ask for those answers separately and preserve the applicable terms and configuration records.
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AI-provider terms are only one layer. Epic’s current Terms of Service restrict using code or content extracted from Licensed Products as training input for a Generative AI Program, or as prompt-based input where that program trains on input data. Confirm that the terms apply to the specific product and workflow before submitting extracted material. This is an Epic-specific restriction; it should not be generalized to every engine.
For third-party assets, marketplace content, licensed characters, voice or likeness material, and partner-owned code, check the relevant license or contract independently. A studio may be authorized to use an asset in a game without being authorized to send it to an external AI service or let another studio access it.
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Build a review and operating process around the service
Use controls proportionate to the data and the consequences of an error. A low-sensitivity brainstorming prompt does not call for the same review as a code change that can reach a production build or a submission containing personal or confidential information.
- Set approved-use rules. Identify permitted and prohibited data categories, who can approve exceptions, and which tools or features are authorized.
- Review outputs. Have people review generated code and assets; check provenance and applicable licenses before incorporating them into a project.
- Secure generated or agent-executed code. Apply security review before code is merged or executed, with particular care when a system can act on repositories or development environments.
- Escalate sensitive cases. Route personal data, confidential material, and high-impact uses to the appropriate security, legal, privacy, or project owner.
- Keep records. Maintain an inventory of approved systems and models, a decision log, and an approved-use policy. Record meaningful changes in settings, contracts, or intended use.
NIST SP 800-218A, published July 26, 2024, supplements the Secure Software Development Framework with AI-specific practices for model producers, system producers, and acquirers. Its recommendations include recording security requirements, considering data-classification policy, and communicating requirements to third parties. It can help structure procurement and development controls; it is not a substitute for the studios’ own agreements.
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Translate the intended workflow into contract terms rather than relying on an informal understanding that the studios will “keep data separate.” Address the following points explicitly:
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- Which studios, teams, projects, and data categories may use the service, and for what purposes.
- Whether prompts, uploaded files, outputs, interactions, and feedback may be retained, reviewed by people, used for model training or improvement, or shared with subprocessors.
- Retention periods, deletion procedures, data export, and what happens to information when a studio or the whole service is offboarded.
- Identity and access responsibilities, including contractor access, project separation, audit evidence, and any limits on one studio seeing another’s material.
- Data location and subprocessors, plus incident notification, cooperation, and each party’s role in responding to a suspected exposure.
- Ownership and permitted reuse of prompts and outputs, and how third-party rights or restrictions are handled.
- Service termination, changes to terms or settings, and remedies if agreed controls are not met.
Then check that the product’s actual settings implement the agreed configuration. Contract language and interface controls should be treated as complementary checks, not substitutes for each other.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess a particular shared system
- Define the use. Name the studios, projects, AI features, intended tasks, and data categories. Include integrations and any agent permissions, not only the chat interface.
- Draw the data flow. Record every system that receives or stores prompts, files, outputs, or logs, including the provider, orchestration layer, subprocessors, retrieval components, and feedback channels.
- Verify access separation. Use test accounts or administrative evidence to establish which users can retrieve each studio’s data, outputs, and logs. Do not infer isolation from separate project labels.
- Read the applicable terms and configure the account. Resolve training and improvement permissions, retention, human review, deletion, export, data location, and incident notice for the actual product, feature, region, and account.
- Check underlying rights. Review engine, marketplace, partner, and asset licenses for restrictions on AI prompts, training inputs, redistribution, or access by other studios.
- Approve and monitor. Document owners, approved use, review requirements, incident contacts, and offboarding steps. Recheck when a provider changes the service, agreement, or settings.
When comparing two systems, evaluate the same dimensions for each: studio and project isolation; prompt and file retention; training and feedback defaults; permission granularity; identity integration and audit logs; subprocessors and data location; deletion and export; incident notification; code and asset-license handling; human review; and contractual remedies. This is a comparison framework, not a claim that any named product has been independently tested or leads on these measures.
How NIST and EU guidance fit into the decision
NIST SP 800-47 Rev. 1 is useful for structuring information exchanges and related agreements across organizations. NIST AI RMF is a voluntary risk-management framework, not a binding certification or a fixed legal requirement. NIST’s framework page reports that its Generative AI Profile was released July 26, 2024, and that AI RMF 1.0 is being revised; organizations can use the framework as a risk-management aid without treating it as law.
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The European Commission published guidelines on the scope of obligations for providers of general-purpose AI models (GPAI) on July 18, 2025, with those obligations applying from August 2, 2025. The guidance concerns GPAI-model providers. A studio using a provider’s model is not automatically a provider; classification depends on the studio’s conduct, any changes or distribution, and applicable jurisdiction-specific facts. The guidance alone does not determine a particular studio’s obligations.
These frameworks and guidelines help identify questions and responsibilities. They do not decide whether a particular data transfer is permitted under a studio’s contracts, licenses, privacy obligations, or local law.
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