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Game studios should change the AI account tier first, then disable training and feedback where needed, and finally set an administrator-controlled retention period. Keep unreleased source code, design bibles, build files, unannounced features, voice references, and player or employee personal data out of consumer AI accounts. As of 14 November 2026, commercial offerings from OpenAI, Anthropic, Microsoft, Google Workspace, and Amazon Bedrock generally state that customer content is not used for model training by default, but storage, feedback, connectors, safety review, and legal holds remain separate controls.
There is no single studio-wide “AI privacy” switch. The right setting depends on the product, plan, model, endpoint, and whether a user is signed in with a studio identity. Use the sequence below as a practical policy for chat, coding, office, narrative, and localization tools.
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What should a studio change first?
Start with the account tier. Move studio work from personal or free accounts to a business, enterprise, education, API, or cloud deployment whose contract says customer content is not used for training by default. Then disable optional feedback and model-improvement programs, set a short retention policy, restrict connectors, and enforce single sign-on so staff cannot quietly return to personal logins.
- Classify the material. Treat unreleased builds, source repositories, design documents, prompts describing unannounced features, voice and likeness references, player records, minors’ data, employee data, and strategic documents as confidential or personal data.
- Use a managed workspace. A business contract is stronger than asking every employee to remember an opt-out. Consumer settings do not protect a personal login used outside the managed tenant.
- Turn off training, feedback, and development programs. These are often separate switches. A training opt-out does not necessarily disable ratings, safety review, telemetry, or a coding assistant’s environment capture.
- Set retention centrally. Choose the shortest period that supports incident response and legal obligations. “Not used for training” does not mean “deleted immediately.”
- Limit context. Review repository, Drive, Jira, Slack, Perforce, and other connectors. A connector can expose substantially more project information than the prompt appears to contain.
Which AI data is too sensitive for a consumer account?
Unreleased game assets and personal information should not be pasted into a publicly available consumer bot, even after a user turns training off. Secondary summaries of CNIL guidance describe consumer services as potentially acceptable for non-confidential work when reuse is disabled, but recommend stronger controls such as on-premises deployment for client data, employee data, and strategic documents. Australia’s OAIC is similarly summarized as recommending that organizations avoid entering personal, especially sensitive, information into public generative-AI tools.
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For a studio, that means a public account may be suitable for a generic explanation of a programming concept or a fictional example. It is not an appropriate place for a player support export, a minor’s account details, an unreleased level layout, a private voice sample, or a repository containing credentials. Google’s unpaid Gemini API terms carry the plain warning: Do not submit sensitive, confidential, or personal information to the Unpaid Services.
The warning is a useful baseline for every consumer service.
Which plans keep studio content out of model training by default?
Several managed products state that customer content is excluded from training by default, but the exact scope differs. OpenAI’s enterprise privacy page says, We do not train our models on your data by default
and covers Business, Enterprise, Healthcare, Edu, Teachers, and the API Platform. The same page says customers control retention on Enterprise, Healthcare, and Edu. It does not make that promise for Free, Go, Plus, or Pro consumer accounts.
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Personal products are different. Consumer ChatGPT may use content unless the user turns training off. Claude Free, Pro, and Max use chats and coding sessions when the user permits model improvement, with an associated retention period of up to five years. Personal Gemini uses activity for improvement while Keep Activity is on. Individual Copilot plans began using inputs, outputs, snippets, and surrounding context for training unless users opt out on 24 April 2026. Use the managed plan so the protection is contractual and enforceable, not a per-user preference.
How should a studio configure ChatGPT and Codex?
On a personal ChatGPT or Codex account that may still receive work, turn off both relevant controls and avoid feedback on work-related answers. In ChatGPT, open Settings → Data controls and disable Improve the model for everyone. OpenAI also labels the equivalent privacy-portal choice Do not train on my content; its help guidance says either option is sufficient for ChatGPT conversations and Codex tasks.
Codex has an additional control. OpenAI’s help guidance identifies a separate Include environments setting, and changing the ChatGPT training toggle does not change it. Anyone allowing Codex to inspect a repository should open the Codex settings and confirm that environment inclusion is disabled unless the studio has explicitly approved repository context. The guidance read in November 2026 did not establish whether the setting is on by default or exactly which repository and configuration files a full environment includes, so treat it as a setting to verify, not an assumption.
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Do not use thumbs-up or thumbs-down on a conversation containing studio material. OpenAI guidance reported in the available data says the entire conversation associated with feedback may be used for training even after an opt-out. Temporary Chat can prevent use for model improvement while it remains temporary, but it may still be retained for up to 30 days for safety. An opt-out applies to new conversations; it cannot remove content that was already incorporated into a training run.
For a studio-owned Business, Enterprise, or Edu workspace, the main jobs are retention, identity, and access control rather than hunting for a training toggle. Keep the API organization’s data-sharing option disabled unless legal and security teams have deliberately approved it. OpenAI’s API data has not been used for training unless the customer opts in since 1 March 2023, according to the cited vendor documentation.
What should change in Claude?
On consumer Claude, open Settings → Privacy and disable Help improve our AI models (also called model improvement). If model improvement is allowed, de-identified consumer chats can be retained for up to five years; opting out uses the standard 30-day window described in the cited summaries. Incognito chats are not used for training even when the account-wide toggle is on, but safety-flagged conversations can still be retained and reviewed under the safety policy.
On Claude Team or Enterprise, training is off by default, but feedback is a separate exception. An Owner should open Organization settings → Data and Privacy and turn off Rate chats. If users can rate conversations, Anthropic may retain the associated conversation for up to five years and may use it for training, including on a business plan.
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Zero-data-retention customers need a further exception review. Anthropic’s covered-model policy, effective 9 June 2026, states: Prompts submitted to, and outputs generated by, covered models are retained for 30 days to support our safety work, on every platform where these models are offered.
The floor applies to covered Mythos-class models and later designated models, including the listed Fable 5 series, across eligible Claude Console, Claude Code Enterprise, AWS Bedrock, Google Cloud Agent Platform, and Microsoft Foundry deployments. The retained data is normally inaccessible to Anthropic personnel; automated safety flags can trigger controlled, logged review. Flagged content and legal records can last longer.
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Anthropic’s Development Partner Program for Claude Code can retain sessions for up to two years, and cited comparisons report that flagged content may be held up to two years and trust-and-safety classification scores up to seven years. Leave development-partner participation off unless the studio has a documented reason and has accepted those clocks.
What changed for coding assistants in 2026?
GitHub Copilot’s individual plans require special attention. From 24 April 2026, Copilot Free, Pro, Pro+, and Max began using inputs, outputs, code snippets, and associated context for training unless the user opts out. On an individual account, open the profile menu, choose Copilot settings, and set Allow GitHub to use my data for AI model training to Disabled. The safer studio policy is to move programmers to Copilot Business or Enterprise, where the no-training protection comes from the agreement and the personal toggle is not the control.
Other coding products also have distinct switches:
- Cursor: turn on Privacy Mode. It is available on Free and Pro, but the cited source could not establish the new-account default, so verify the screen for every account.
- JetBrains AI: collection is described as a default only on the free non-commercial license and can be disabled. Commercial use is not collected without consent.
- Amazon Q Developer: the Free Tier collects content until Amazon Q: Share Content is turned off; the Pro tier does not collect content.
- Claude Code: consumer behavior follows Claude’s model-improvement setting; Team, Enterprise, and API deployments are not used for training by default.
- Google Antigravity: usage records can be used for improvement and may be reviewed by people. Turning telemetry off does not stop data needed to provide the service or for safety.
A November 2026 online claim that Codex training had silently been re-enabled was not confirmed in the cited material. The practical control is a scheduled review of both Codex switches, not reliance on an unverified post or a one-time setting.
How should studios configure Gemini and Microsoft Copilot?
For a personal Gemini account, open gemini.google.com → Activity and turn off Keep Activity. With it on, the cited sources describe an 18-month adult default, with choices of three months, 36 months, or no limit. Turning it off reduces ordinary retention to 72 hours; temporary chats are not used for training. A conversation read by a human reviewer can be retained for up to three years and disconnected from the account, so sensitive studio content still belongs in a managed environment.
Gemini in Google Workspace does not use Workspace prompts, content, or responses to train models without permission, but administrators still control history. The cited ranges are up to 36 months for Gemini app history and 90 days to no limit for Gemini features inside Gmail or Docs. Do not assume Gemini Notebook, formerly NotebookLM, inherits those controls: the cited comparison says it copies Drive files into its own storage, does not follow organization data-region settings, lacks Workspace data-loss-prevention integration, and sits outside Google’s listed ISO, SOC, FedRAMP, and HIPAA business-associate coverage.
Microsoft 365 Copilot administrators should set retention in Microsoft Purview and treat optional feedback separately. Web-search queries go to Bing under separate terms. Some advanced Claude models inside Copilot require an additional administrator opt-in that brings Anthropic retention terms; leave that option off for unreleased content unless legal has approved it. The consumer Microsoft Copilot app was updated on 18 August 2026 so prompts, responses, and files are not used to train foundation models, but older app versions may still follow the prior opt-out behavior and an 18-month history default. Personal accounts should not be the studio’s control plane.
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Which settings matter for narrative, localization, and writing tools?
Narrative and localization text is confidential game content, even when it contains no personal data. The cited October 2026 review identifies these practical changes:
- Notion AI: training is off by default on all plans under Notion’s contracts. Model-provider retention is described as none on Enterprise and up to 30 days on other plans; a deleted page remains recoverable for 30 days.
- DeepL Free: text is used to train the service and there is no opt-out. Use DeepL Pro for studio text; its cited terms delete text after delivery and do not use it to improve the service.
- Grammarly: Free, Premium, and single-user Pro enable Product Improvement and Training by default, including generative features. Turn it off in settings. Online-purchased Team accounts start on and an administrator can disable it; sales-bought Business, Education, Pro, and Enterprise accounts start off. Writing statistics may still be collected.
- Perplexity: Free, Pro, and Max have AI data retention on by default. Disable it under Settings → Preferences; the change applies to data collected afterward. Enterprise is described as non-training.
- Mistral Vibe: Free and Pro use interactions for training unless Allow my interactions to be used to train our models is disabled. Enterprise is off by default, Team administrators can disable it organization-wide, and the API has a separate Anonymous improvement data control. Paid API zero retention requires approval and does not apply to the Vibe chat app.
What is the difference between turning training off and deleting data?
Training status and retention are independent. A provider can promise not to train on new prompts while storing them for abuse monitoring, search history, workspace history, feedback review, backups, or legal holds. Conversely, a short retention period cannot undo training that occurred before an opt-out.
OpenAI’s zero-data-retention announcement says eligible API customers’ prompts and responses are not retained after processing, but eligibility is not automatic. Standard abuse-monitoring logs can last up to 30 days. Stored Responses retain data for at least 30 days when the store parameter is enabled by default, and conversations, project files, custom-GPT files, ChatKit threads, and assistants have separate storage behavior. Images flagged as suspected child sexual abuse material remain subject to legal retention even under the stated exception.
Anthropic zero data retention is arranged per organization and covers selected Messages and Token Counting APIs, not batch processing, code execution, Files, MCP connectors, Managed Agents, or consumer, Team, and Enterprise chat interfaces. Google’s paid Gemini API is described as retaining abuse logs for 55 days and offering no guaranteed zero-retention mode there; Vertex AI is the cited route for stronger enterprise controls. Grounding with Google Search has its own roughly 30-day storage that cannot be disabled on the paid tier.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should identity, connectors, and shadow accounts be controlled?
Require SAML single sign-on, managed domains, and role-based feature access wherever the product supports them. OpenAI lists SAML SSO, fine-grained feature controls, AES-256 encryption at rest, TLS 1.2 or higher in transit, and SOC 2 among its enterprise controls. Anthropic’s enterprise materials list custom retention, US-only inference, audit logs, a compliance API, IP allowlisting, tenant restrictions, and customer-managed encryption-key options; confirm availability and setup in the contract.
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Connectors deserve the same review as prompts. A Jira, Drive, Slack, or Perforce integration can send every file permitted by its service account. Use least-privilege scopes, separate production and development projects, exclude secrets and player exports, and log connector access. A managed workspace cannot protect a personal account, an over-broad connector, or an employee who pastes content into an unrelated public bot. The cited Gartner figure—69% of organizations suspect or have evidence of prohibited public generative-AI use—illustrates why technical enforcement matters, although it is not a game-studio-specific statistic.
Should a studio block AI crawlers on its public website?
Yes, but treat crawler controls as a separate publishing decision. On 30 October 2026, OpenAI, Google, Microsoft, and Anthropic announced support for a shared robots.txt token, __usage_ai-training. The described rule is:
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Disallow: /Anthropic had begun implementing it. OpenAI said implementation would follow, Microsoft’s reported target was 1 February 2027, and Google’s target was 2027. Existing controls such as GPTBot and Google-Extended still matter until each company is live. The signal applies to a studio website, patch-note blog, or public wiki; it does not change private chatbot retention or remove material already used in training. The reports from The Next Web and OpenTools describe the rollout and its limits.
What should be in the studio’s AI policy?
Publish a short policy that employees can follow and administrators can enforce:
- Define prohibited inputs: player and employee personal data, minors’ data, credentials, unreleased builds, confidential design documents, private voice or likeness material, and regulated client information.
- List approved workspaces and plans, including the exact domains, tenants, API projects, and models.
- Require SSO and prohibit personal accounts for studio work.
- Record training, feedback, telemetry, connector, retention, residency, and zero-retention settings for every approved tool.
- Set a review cadence—at least quarterly and whenever a vendor changes terms, model families, or defaults.
- Define an incident path: revoke tokens, disconnect integrations, preserve required evidence, request deletion where possible, and notify legal or privacy staff.
- Require vendor contracts and data-processing addenda to override assumptions drawn from marketing pages or secondary comparisons.
Re-check vendor documentation before freezing the policy. The cited material spans 8 January to 11 November 2026, and defaults, covered-model lists, and residency regions have changed repeatedly during that period. The settings above are operational guidance, not legal advice.
Frequently Asked Questions
Does turning off AI training delete a game studio’s old chats?
No. An opt-out normally affects new collection or future training, not material already included in a training run. Deletion requests, workspace retention rules, backups, safety logs, and legal holds are separate processes and may have different deadlines.
Can zero data retention cover an entire AI workspace?
Usually not. Provider programs are eligibility-gated and limited to named APIs, models, or features. Files, conversations, batch jobs, code execution, connectors, abuse logs, and covered safety models may remain outside the promise.
What should a studio do when an employee used a personal AI account?
Stop further use, preserve the minimum incident details needed for investigation, revoke exposed credentials, and ask the provider about deletion and retention. Move the workflow to an approved tenant and review the account’s feedback, connector, and history settings rather than assuming an opt-out erased the material.
Are public game websites covered by the same privacy settings as AI chats?
No. A robots.txt training token controls crawler access to public pages; it does not govern prompts, private files, connectors, or retention inside an AI product. Keep both controls in the publishing and AI-governance checklists.
