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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThe safest approach is layered control, not a single “don’t train on my data” switch. Identify the exact AI product and account, separate model training from retention, memory, human review and connected-data access, then minimize sensitive inputs and enforce deletion, permissions and contractual controls. A training opt-out can leave prompts stored for operations, details remembered for personalization, or connected files available to the system.
What “reuse” means in an AI service
People use “reuse” to describe several different data flows. They have different controls and different risks, so evaluate each one separately.
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| Data use | What it means | Control to look for |
|---|---|---|
| Model training or improvement | Your prompts, files or outputs may be used to change or evaluate future models. | Training opt-out, opt-in terms, or a business/API no-training commitment. |
| Operational retention | Content is kept temporarily for delivery, debugging, abuse prevention, security or legal reasons. | Retention period, endpoint exceptions, deletion process and legal holds. |
| Memory and personalization | The service stores facts about you or your preferences to tailor later responses. | Memory controls, a memory list, export and deletion options. |
| Connected-data retrieval | The AI reads files, mail, repositories or other systems when answering a question or taking an action. | Permission checks, sensitivity labels, connector scope and action approvals. |
| Human access | Authorized staff or contractors may inspect content for support, safety, security or legal reasons. | Access policy, auditing, support terms and incident notification. |
| Data-subject use | Personal information may be used to train a model or appear in generated material about a person. | Privacy notices, retention explanations and rights procedures. |
Turning off training addresses only the first row. It does not automatically erase conversation history, remove saved memories, revoke a connector, or prevent every form of review.
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Privacy behavior can change between a consumer chatbot, a managed workplace application, an API endpoint and even two features inside the same product. Microsoft’s documentation describes exclusions and opt-out behavior that depend on the Copilot product and account. OpenAI documents separate controls for its business products and API. Read the terms and settings for the specific tier, region, endpoint and administrator configuration you will use, rather than relying on a provider-wide slogan.
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- Record the product name, edition, account type and region.
- Identify whether an administrator, a workspace owner or an individual controls privacy settings.
- List every feature that will touch the data: chat, file upload, memory, browsing, connectors, agents, logging and evaluation.
- Save the current privacy and retention documentation with the approval record. Product behavior and eligibility can change.
For current API controls, consult OpenAI’s data-controls documentation. For Microsoft products, compare the Microsoft 365 Copilot privacy documentation with the consumer Copilot privacy FAQ.
Protecting personal use of consumer AI
1. Find the training or improvement setting
Open the service’s privacy, data-controls or account-settings page and locate the control for using conversations to improve models. If you do not want that use, disable it and record the date. Check whether the switch applies only to future conversations, whether it excludes uploaded files, and whether a separate feedback or evaluation program remains enabled.
Microsoft’s consumer Copilot FAQ says conversations made after opting out are excluded from model training unless the user opts back in, while also listing product and account exclusions. The setting therefore needs to be confirmed in the exact Copilot experience you use; it should not be generalized to every Microsoft service.
2. Review memory separately
Training and personalization are different purposes. Microsoft states that a user can opt out of model training while leaving personalization enabled, allowing the service to remember key details for tailored responses. Open the memory or personalization panel, inspect saved items, delete anything unnecessary and disable the feature if retaining those details is not acceptable.
3. Delete conversation history when appropriate
Deleting a chat is a retention action, not proof that the chat was never used for training. Conversely, a training opt-out does not necessarily delete existing history. Use the product’s history-management controls to remove conversations, uploaded files and saved memories according to its documented process. Microsoft 365 Copilot provides user deletion of Copilot activity history; administrators can apply broader retention and deletion policies through Microsoft Purview.
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4. Keep high-impact secrets out
Do not paste passwords, API keys, recovery codes, payment-card data, identity documents, medical records, private legal correspondence or unreleased business material into a consumer service unless its handling has been verified and the use is authorized. Redaction, synthetic examples and local processing reduce exposure, but they do not replace checking the service terms.
Controls organizations should establish before rollout
Publish an approved-tools and data-classification policy
Make a short list of permitted AI services and map data classes to allowed environments. For example, public information may be used in an approved consumer tool; internal information may require a managed workspace; regulated records, credentials and trade secrets may require a specifically contracted API or may be prohibited entirely. State who can approve an exception and how it is documented.
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Verify, in writing, whether prompts and outputs are used for training by default, how support and safety review works, which subprocessors are involved, where data is processed, and how deletion requests and legal holds operate. OpenAI states that business and API data is not used for training by default, but its API documentation describes retention exceptions and approval requirements for stronger controls. Treat “not used for training” as one contract term, not as a guarantee of zero storage or zero access.
Apply least privilege to people and connectors
Grant the AI only the repositories, folders, mailboxes and actions it needs. Remove dormant accounts, use groups rather than broad tenant-wide access, and review connector scopes after every change. Test retrieval with accounts that have different permissions. Microsoft says Microsoft 365 Copilot honors usage rights conveyed through Purview Information Protection encryption or restricted permissions; your configuration still needs correct labels and source permissions.
Set retention and deletion rules
Define how long prompts, responses, uploaded files, audit records and feedback remain available. Align those periods with legal, security and business requirements, then configure the product or its management layer. Microsoft documents Purview controls for Copilot interaction data. Record who can place a legal hold, who can execute deletion and how completion is verified.
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Control human review and support access
Ask which personnel can inspect content, for what purposes, in which regions and under what logging. Limit support access, require case-based approval where possible and retain access logs. Include notification and investigation duties in the contract.
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OpenAI’s API documentation says inputs and outputs may be retained for up to 30 days for service provision and abuse monitoring, subject to endpoint and feature exceptions. That period is not a universal promise for every API feature, so check the endpoint-specific table and your account terms.
Eligible customers can request Zero Data Retention (ZDR) or Modified Abuse Monitoring, but approval is required and limits apply. ZDR means qualifying content is not retained for the covered purpose; it does not mean that every endpoint or feature is incapable of storing application state. The documentation warns that some features may retain state and that customers have responsibilities for configuring and using the controls correctly.
- Inventory every endpoint, tool and feature in the application.
- Check whether each is supported by ZDR or Modified Abuse Monitoring.
- Identify safety, abuse-monitoring, billing and legal exceptions.
- Request approval before sending production data.
- Test deletion and logging behavior with non-sensitive records.
- Reassess after an API, feature or policy change.
OpenAI announced ZDR for frontier models on August 19, 2026, and posted a September 22, 2026 update about a phased Private Safety Processing rollout. Availability is therefore not universal; confirm current eligibility before treating either control as deployed.
Microsoft Copilot: separate consumer and work scenarios
Consumer Copilot
The consumer FAQ describes an opt-out that excludes future conversations from training unless the user opts back in, while allowing personalization to remain enabled. It also identifies account and product exclusions. Review the FAQ for the account you actually use, then inspect training, personalization, memory and history settings individually.
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Microsoft 365 Copilot
Workplace Copilot operates in an organizational permission environment. Microsoft says it honors relevant Purview Information Protection usage rights, including rights conveyed through encryption or restricted permissions. Administrators can manage stored interaction data with Purview, while users can delete Copilot activity history. These controls protect against unauthorized retrieval and excessive retention; they do not make every connected source safe by default. Correct permissions, labels and retention policies remain necessary.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use this comparison checklist before choosing a service
| Dimension | Questions to answer |
|---|---|
| Training | Are prompts or outputs used to train or improve models by default? Is there an opt-in or opt-out? Does it cover past data, future data, uploads and feedback? |
| Retention | How long are prompts, outputs, logs and files kept? What operational, safety, abuse-monitoring, legal or endpoint exceptions apply? |
| Memory | Is conversational memory separate from training? Can users view, disable and delete saved details? |
| Human access | Which support, safety, security or legal personnel can view content? Is access logged and limited? |
| Connected data | Does retrieval obey source permissions, encryption and sensitivity labels? Can the system take actions, and are approvals required? |
| Deletion and rights | Can users and administrators delete history? How are erasure, access and correction requests handled for training data and generated outputs? |
| Location | Where is content stored and processed? Which residency option applies to this tier and feature? |
| Strong controls | Is ZDR or an equivalent available for this customer, endpoint and feature? What approval, exclusions and customer obligations apply? |
A service should not be labelled simply “private” or “not private.” Compare these dimensions against the sensitivity of the data and the consequences of disclosure.
Connected repositories are a separate exposure
An AI assistant may never train on a document yet still reveal it in a response if a connector can retrieve it. Before enabling search, grounding or actions, map each source, permission group and sensitivity label. Test both allowed and denied cases, including a user who should not see a confidential file. Remove inherited broad permissions and stale shares in the source system; the AI cannot reliably enforce a restriction that the source does not express.
For action-taking agents, require confirmation for sending messages, changing records, approving transactions or downloading files. Log the request, retrieved sources and resulting action so an investigator can reconstruct what happened.
Retention, deletion and rights
Write a retention schedule that distinguishes chat history, model-improvement data, application logs, safety records, files and audit trails. Deleting one category may not delete another. Ask the provider how backups, abuse-monitoring records and legal holds are handled, and what evidence it supplies after a deletion request.
UK organizations must also consider the Information Commissioner’s Office (ICO) guidance. The ICO says privacy information should explain processing purposes, retention periods and recipients. When data is collected directly, people should be informed at collection and before it is used to train a model or applied to people; for data obtained elsewhere, the guidance generally describes a reasonable period and no later than one month, subject to exceptions. See the ICO transparency guidance.
The ICO also says erasure requests involving training data must be considered. Model erasure is not automatically required unless the model contains the personal data or can be used to infer it. The applicable test depends on the facts and jurisdiction; consult the ICO guidance on individual rights and obtain legal advice for regulated cases.
What to do after an accidental disclosure
- Contain access: revoke shared links, disable the affected connector or account and preserve relevant logs.
- Rotate secrets: change exposed passwords, API keys, tokens and certificates where warranted; invalidate sessions.
- Identify the data: record exactly what was submitted, to which product, account, endpoint and recipients.
- Request deletion: use the service’s history, memory and administrator controls, and ask the provider about retention exceptions and backups.
- Report internally: follow your security, privacy and incident-response procedures.
- Assess notification duties: determine whether contractual, employment, sectoral or data-protection rules require notice.
- Prevent recurrence: tighten DLP rules, connector permissions, approved-tool lists and user training.
Common mistakes that defeat privacy controls
- Assuming one provider’s consumer policy applies to its API or enterprise product.
- Treating a training opt-out as a deletion command.
- Leaving memory enabled because training is disabled.
- Connecting an entire drive or mailbox when a project folder would suffice.
- Ignoring support, safety and abuse-monitoring access.
- Relying on ZDR without checking endpoint support, approval and stateful features.
- Using real customer records to test prompts or connectors.
- Failing to review settings after a product, account or contract change.
A practical approval decision
Approve an AI use only when all of these statements are true:
- The exact product, account, endpoint and region are documented.
- The data classification permits that service and feature.
- Training, retention, memory, human access and connected-data behavior are understood.
- Permissions are least-privilege and retrieval tests pass.
- Deletion, rights requests and incident contacts are documented.
- Any stronger control, such as ZDR, has been approved and its exclusions are accepted.
- Users know what must never be entered.
If any answer is unknown, use redacted or synthetic data until the provider and your organization resolve the gap. This is safer than assuming a privacy label covers every route by which information can be stored or reused.
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