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There is no single “most private” AI assistant. A useful comparison matches the exact product, account type, country, settings and data category, then separates model-improvement use from the retention needed to operate and secure the service. For example, a chat excluded from training can still be held briefly for abuse detection, reviewed after a safety flag, or retained because you submitted feedback. Use the provider’s current settings and policy language as a worksheet rather than treating labels such as “private,” “temporary” or “deleted” as complete answers. The examples below cover current consumer experiences for ChatGPT, Claude and signed-in Gemini Apps; business workspaces, APIs and managed school accounts can follow different terms.
What exactly should you compare before judging an AI assistant’s privacy?
Start by fixing the scope. “The assistant” may mean a consumer web chat, a mobile app, a managed workspace, an API account or a connected feature inside another service. Those surfaces can have different defaults, administrators and deletion rules.
- Product surface: Record the assistant name, app or website, voice or coding feature, and whether connected applications are enabled.
- Account relationship: Note personal, business, Enterprise, Education, healthcare, API, work or school status. Do not transfer a commercial or API promise to a free consumer account.
- Location and eligibility: Capture country or region, age eligibility and the date checked. Providers can change controls by jurisdiction or account age.
- Data involved: Separate prompts, generated answers, chat history, uploaded files, images, audio, feedback, memories, connected-app content and account identifiers.
- Purpose: Identify whether each item is retained for service delivery, security, abuse investigation, legal compliance, support, human review, feedback handling or model improvement.
A policy’s headline training choice answers only one question: whether a provider may use eligible content to improve models. It does not answer how long the service keeps the content to return an answer, investigate misuse or process a report.
How do I make a repeatable comparison?
Use the same sequence for every assistant. Save the policy title, setting name and date beside each answer so a later change is visible.
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- Identify the account and region. Write down the exact plan, workspace ownership, country and age category. If an administrator controls retention, record that instead of assuming the consumer setting applies.
- Capture defaults before changing anything. Note whether history, activity logging, memory and model-improvement use are on or off when a new account is created.
- Read the control’s scope. Check whether opting out affects future prompts only, prior conversations, uploads, coding sessions, memories, feedback or connected services. Look for wording about “new or resumed” chats.
- Map every retention path. Create separate entries for ordinary history, safety copies, flagged conversations, legal or dispute holds, feedback, human review and data already incorporated into a completed training run.
- Test deletion language, not just the button label. Record when content disappears from the interface, when backend deletion is promised, and which copies or model-training artifacts are excluded.
- Repeat the check for uploads and integrations. A file sent through a connected cloud service, a voice transcript or an app action may follow additional settings and terms.
- Save the evidence. Keep the policy name, revision date, exact interface label and a short quotation. Recheck before relying on the result because labels and retention statements change.
What do the current consumer policies say?
The following examples are provider-stated terms for consumer services, checked against official guidance current on October 26, 2026. They are not independent audits or a cross-provider privacy score.
ChatGPT consumer services
OpenAI says the controls available to a consumer depend on the plan, account and workspace settings. Model-improvement preferences and memory are separate controls, so turning off one should not be treated as turning off the other.
Temporary Chats do not appear in history, do not create or update memories and are not used to improve models while they remain temporary. OpenAI may retain a copy for up to 30 days for safety. If you save a Temporary Chat, it becomes a regular chat and then follows the applicable settings for that account.
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Claude consumer plans
Anthropic’s consumer retention guidance says a deleted conversation disappears from chat history immediately and is deleted from backend systems within 30 days. That is a backend-deletion commitment, not a claim that every derivative or separately retained copy disappears at the same moment.
If a user allows Claude to use chats or coding sessions to improve Claude, Anthropic says that data may be kept in de-identified form in model-training pipelines for up to five years. The setting applies to new or resumed chats after it is enabled. Turning it off prevents future training use, but does not remove data already in training or models that have already been trained.
Incognito chats are excluded from model improvement. Anthropic also describes longer retention for inputs, outputs and safety scores when a conversation is flagged, as well as retention required for legal matters, disputes or policy enforcement. Consumer Free, Pro and Max terms should not be confused with commercial or API commitments.
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Signed-in Gemini Apps
Google’s Gemini Apps Privacy Hub says Keep Activity is on by default for adults. Activity is set to auto-delete after 18 months by default, with options for three months, 36 months or no automatic deletion.
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With Keep Activity on, activity may be used to provide, develop and improve services, including training generative-AI models. Some chats may be reviewed by people or trained service providers. Google says chats reviewed for improvement or safety are disconnected from the account before review and retained for up to three years; deleting activity does not delete those reviewed copies.
With Keep Activity off, future chats do not appear in Gemini Apps Activity and are not used to train models unless you submit feedback. Google says those chats can remain associated with the account for up to 72 hours for service delivery, feedback processing and protection of Google, users and the public.
Temporary chats are not used to train Google AI, but Google still processes them to answer requests and protect the service, potentially with human review. Feedback can include recent conversation context and uploaded or connected content and can be retained for up to three years. Connected-app data, other Google-service settings and work or school accounts add scope that a single Gemini toggle does not control.
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This table records the principal provider-stated defaults and limits. “Not stated” means the cited consumer guidance does not establish a comparable value.
| Comparison point | ChatGPT consumer services | Claude consumer plans | Gemini Apps |
|---|---|---|---|
| Training or improvement control | Model-improvement preference; availability depends on plan, account and workspace. | Optional model-improvement setting; applies to new or resumed chats after it is enabled. | Keep Activity setting; activity may support service improvement and generative-AI training when on. |
| Temporary or equivalent mode | Temporary Chat stays out of history and memory and is excluded from improvement while temporary. | Incognito chats are excluded from model improvement. | Temporary chats are not used to train Google AI. |
| Provider-stated short-term retention | Temporary Chat copy: up to 30 days for safety. | Deleted consumer conversation leaves backend systems within 30 days. | Keep Activity-off or temporary chats: up to 72 hours for service and protection purposes. |
| Longer training-related retention | Not stated in the cited consumer guidance. | De-identified chats or coding sessions allowed for improvement: up to five years in training pipelines. | Reviewed chats and related data: up to three years. |
| Default activity/history setting | Varies by plan, account and workspace; check the account controls. | Not stated as one universal consumer default in the cited guidance. | Keep Activity on by default for adults; auto-delete set to 18 months. |
| Feedback effect | Check the account’s feedback and improvement controls separately from memory. | Check whether a feedback or flagged conversation enters a separate safety or legal process. | Feedback may include recent context and uploaded or connected content and can be retained up to three years. |
The figures are not directly comparable: one describes a safety copy, another a deletion timetable, another a training pipeline or reviewed data. A larger number does not automatically mean a less private service because the purpose and data category differ.
What is the difference between model improvement and operating the service?
Model improvement is a future-use decision. Operating the service includes receiving your prompt, generating a response, preventing abuse, diagnosing failures, handling a complaint and complying with law. A no-training setting can therefore coexist with limited operational retention.
Training exclusion
Ask whether the exclusion covers only future prompts or also previous chats, resumed threads, coding sessions and files. Determine whether a setting change affects material already selected for training or models already trained; Anthropic explicitly distinguishes those cases.
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Temporary or history-off modes can still be processed for security. OpenAI’s Temporary Chat copy may be held for safety, and Google says temporary chats may receive human review for service protection. A “temporary” label describes the user-facing history behavior, not zero processing.
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Human review
Find out what triggers review, whether reviewers are employees or trained service providers, whether content is disconnected from your account, and how long reviewer copies remain. Google’s policy says reviewed Gemini chats are disconnected before review but can remain for the stated review-retention period.
Legal and policy enforcement
Deletion promises commonly contain exceptions for legal obligations, disputes and enforcement. Claude’s consumer guidance expressly describes longer retention for those purposes. Treat an ordinary deletion window as the normal path, not an unconditional guarantee.
How should I compare deletion, history and auto-delete?
Record three separate events: disappearance from the interface, removal from backend systems and removal from derivative systems such as review stores or completed training. They may occur at different times.
- Visible history: Can you still open the conversation, search it or restore it?
- Backend deletion: Does the provider promise a period such as “within 30 days,” and does that promise cover all copies?
- Safety and review copies: Are flagged or sampled conversations retained under a separate schedule?
- Training artifacts: Does opting out or deleting a chat affect data already used in a training run? If not stated, do not assume reversal.
- Account closure: Is deleting one chat different from deleting the entire account and its associated feedback or connected-service records?
For Gemini, auto-delete is an activity schedule rather than a promise that every reviewed copy disappears on that date. For Claude, a deleted conversation’s backend timetable is distinct from the longer period described for de-identified material used in model-improvement pipelines.
Which data categories need their own line in your worksheet?
Prompts, answers and chat history
Log the default history state, the effect of a temporary mode and whether resumed conversations inherit a newly changed setting. “History off” can mean hidden from the account while a short operational copy remains.
Uploads and generated files
Ask whether an uploaded document is retained with the conversation, copied into a safety or feedback record, or governed by a connected storage service. Do not infer file handling from text-chat rules.
Memory and personalization
Memory can be a separate store from chat history. Check whether a temporary mode prevents creation and whether turning memory off removes existing entries or merely stops new ones. OpenAI’s consumer guidance specifically separates memory from model-improvement preferences.
Feedback
A thumbs-up, report or written comment can attach recent context to your submission. Google says feedback may include conversation context and uploaded or connected content. Record the retention period and whether feedback changes the training or review path.
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Voice, images and coding sessions
Audio, images and code can have feature-specific processing. Claude’s improvement setting explicitly refers to coding sessions; other products may document voice or image handling separately. Capture each feature instead of assuming the text-chat rule covers it.
Connected apps
When an assistant reads mail, documents, calendars or another service, both systems’ settings matter. Gemini’s policy warns that connected-app data and other Google-service controls sit outside a single Keep Activity switch.
What changes for work, school and API accounts?
Do not use consumer screenshots to evaluate an organization’s deployment. Managed workspaces can set retention centrally, limit exports, control connectors and preserve records for legal or regulatory reasons. School accounts may be administered under separate age and data rules.
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API terms generally describe data handling for requests sent by a developer, not the consumer chat product. Enterprise, Education, healthcare and business offerings can also change default training use and administrator visibility. Obtain the contract or administrator policy for the exact service, region and workspace before entering confidential material.
What are the most common comparison mistakes?
- Ranking by one number: A 30-day safety copy, a 30-day backend deletion promise and a three-year review store measure different things.
- Assuming “not used for training” means “not retained: Service delivery, security and feedback processing can still require short-term access.
- Treating deletion as instantaneous everywhere: Interface removal may precede backend deletion, legal holds or de-identified training records.
- Ignoring resumed chats: A setting can apply to new or resumed conversations rather than retroactively changing older data.
- Combining personal and managed accounts: Workspace administrators and API contracts can override consumer expectations.
- Forgetting feedback and connectors: A report or connected document can carry more context than the message you intended to share.
- Quoting an old interface label: Settings and policy wording change; record the date and recheck before making a decision.
How can I turn the comparison into a practical decision?
Set a threshold for each type of data instead of selecting a universal winner.
- Classify the information. Mark it public, internal, personal, regulated or confidential.
- Reject unsuitable surfaces. If a provider’s consumer terms do not meet your organization’s requirements, use an approved managed or API deployment rather than relying on a temporary toggle.
- Choose the narrowest setting. Disable improvement where available, use temporary or incognito modes for one-off sensitive work, and keep feedback off unless you intend to share context.
- Limit content before sending. Remove names, account numbers, secrets and unnecessary attachments. Redaction reduces exposure regardless of policy.
- Document exceptions. Note safety, legal, review and training-retention paths that remain after your preferred setting is enabled.
- Schedule a review. Recheck policy pages and account labels after major product changes or at least when your data classification changes.
The defensible outcome is a documented fit: “This account, in this region, with these controls, is acceptable for this data category.” It is not a permanent ranking of one assistant over another.
What should a one-page comparison record contain?
Use one row per assistant and keep the wording precise:
| Field | Entry to capture |
|---|---|
| Identity | Product surface, plan, account owner, country, age category and date checked |
| Defaults | History, activity, memory, model-improvement and feedback states |
| Controls | Exact label, scope, whether it affects prior or resumed chats, and who can change it |
| Retention | Ordinary history, temporary mode, safety, human review, legal holds and training pipelines |
| Deletion | Visible-history timing, backend timing, exceptions and treatment of trained models |
| Content types | Text, files, images, audio, code, memories and connected-app data |
| Evidence | Policy or help-page title, revision date, exact quotation and setting path |
What is the bottom-line method?
Compare purposes and data categories, not marketing adjectives. Identify the exact account, record defaults, inspect what each control changes, separate operational and safety retention from model improvement, and document deletion exceptions. The three consumer examples show why no single retention period can serve as a privacy score: the periods concern different stores, triggers and uses.
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Frequently asked questions
Can a provider use a chat after I turn off model training?
Possibly, for service delivery, security, abuse prevention, feedback handling or legal obligations. Turning off future training use does not necessarily erase operational copies or data already incorporated into a completed training process.
Does deleting a conversation remove it from an AI model?
Not necessarily. Deletion may remove the visible chat and ordinary backend copies while leaving separately retained safety or legal records; a provider may also state that already trained models are unaffected.
Are temporary chats safe for confidential secrets?
They reduce ordinary history or training exposure in the documented products, but they still must be processed to answer you and protect the service. Do not enter secrets unless your organization has approved that specific product and account.
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Why can two people see different privacy settings?
Controls can vary by plan, workspace administrator, country, age category, product surface and feature. A personal mobile account and a managed organization account should be evaluated as separate services.
How often should I recheck an assistant’s policy?
Recheck after a major feature, plan or interface change and before using the service for a new data category. Record the date and exact setting label so you can detect a change rather than relying on a remembered screenshot.
Frequently Asked Questions
Can a provider use a chat after I turn off model training?
Possibly, for service delivery, security, abuse prevention, feedback handling or legal obligations. Turning off future training use does not necessarily erase operational copies or data already incorporated into a completed training process.
Does deleting a conversation remove it from an AI model?
Not necessarily. Deletion may remove the visible chat and ordinary backend copies while leaving separately retained safety or legal records; a provider may also state that already trained models are unaffected.
Are temporary chats safe for confidential secrets?
They reduce ordinary history or training exposure in the documented products, but they still must be processed to answer you and protect the service. Do not enter secrets unless your organization has approved that specific product and account.
Why can two people see different privacy settings?
Controls can vary by plan, workspace administrator, country, age category, product surface and feature. A personal mobile account and a managed organization account should be evaluated as separate services.
How often should I recheck an assistant’s policy?
Recheck after a major feature, plan or interface change and before using the service for a new data category. Record the date and exact setting label so you can detect a change rather than relying on a remembered screenshot.
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