There is no universally safest or most private AI provider. The right choice depends on the exact product, account type, plan, region, data, and potential harm if information is exposed or retained. Compare documented controls—not marketing labels—across training use, retention and deletion, human access, security administration, data location, contract terms, safety evidence, and user recourse.
A personal chatbot, a business workspace, and an API from the same company can have materially different defaults and legal commitments. Use the process below to match those differences to your threat model before uploading sensitive information.
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Start with the exact AI product and account
“The provider” is not a sufficient description. A consumer chatbot signed in with a personal account may use different training settings, retention periods, reviewers, subprocessors, and deletion rules than a business workspace or API project. Education, healthcare, and other specialized offerings can add eligibility conditions and contractual controls.
Record these details before comparing services:
- Product name and edition (for example, a consumer app, enterprise workspace, or API).
- Personal, work, school, or managed account status.
- Country or region of the account and where processing is promised to occur.
- Plan, model, endpoints, and any optional features such as voice, file uploads, connectors, or feedback.
- Date on which you checked the documentation.
Never use a business API’s “not used for training” term as evidence about a consumer app, and do not transfer personal-account settings to a work or school deployment.
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Classify the data and the possible harm
Write down what the system will receive and who could be harmed by disclosure, a wrong answer, a prolonged retention period, or an automated decision. A useful starting classification is:
- Public or low-risk: published facts, generic brainstorming, or material that contains no personal or confidential information.
- Routine business material: internal drafts, source code, customer-support text, or non-public plans.
- Personal information: identifiers, employment records, financial details, location data, or private correspondence.
- Regulated or high-impact records: health, education, legal, financial, or biometric information, or data used in decisions about people.
- Credentials and secrets: passwords, API keys, private certificates, recovery codes, or trade secrets.
Define the failure you are trying to prevent. A provider that is acceptable for public text may be unsuitable when an employee, patient, customer, child, or business secret could be identified. If a service cannot meet the minimum requirements for the data, do not upload it; change the workflow or use a separately assessed deployment.
Use the same comparison questions for every provider
Complete one record for each exact product and plan. Mark whether a control is enabled by default, optional, limited to eligible customers, or subject to exceptions.
| Area | Questions to answer |
|---|---|
| Training and improvement | Are prompts, responses, uploads, voice, feedback, or connected-app data used to train or improve models? Is consent an opt-in or opt-out? Does a change apply only to future data? |
| Retention and deletion | What is the default retention? Can it be shortened or disabled? What remains for safety, legal, security, or abuse purposes? Does deleting visible history remove reviewer-held or de-identified copies? |
| Human access | Can employees, contractors, or service providers inspect content? For which purposes, under what account separation, and for how long? |
| Security and administration | Which encryption, MFA or SSO, role controls, audit logs, key-management options, and independent assessments apply to the product you are buying? |
| Location and contract | Where is data stored and processed? Is residency available for your plan and region? What DPA, BAA, or other commitments apply? |
| Safety evidence | Are evaluations dated and model-specific? Are methods, red-team work, external reviewers, deployment safeguards, and known limitations disclosed? |
| Transparency and recourse | Can users report problems, appeal enforcement, export or delete data, and learn about material policy changes? |
Save the relevant privacy notice, retention page, terms, and security documentation with the date and region you checked. A control that is not documented for your service should be treated as unconfirmed, not assumed.
What the documented controls say about major services
OpenAI business and API offerings
OpenAI says inputs and outputs from ChatGPT Enterprise, Business, Edu, Healthcare, Teachers, and the API platform are not used for training by default. Qualifying organizations can configure retention, including API zero-data-retention options, and eligible services offer data-residency and in-region-processing choices. The business documentation also describes encryption, enterprise key management, access management, audit-log capabilities, and compliance material. Eligibility, supported endpoints, and the contract determine whether a particular option applies, so verify the exact service before relying on it. See OpenAI’s business data privacy, security, and compliance documentation.
OpenAI’s security page says its API and ChatGPT business services underwent an independent SOC 2 Type 2 examination. It lists ISO certifications for specified services, including ISO/IEC 42001 coverage for consumer and business products and models, and describes recurring evaluation through benchmarks, adversarial testing, and safety monitoring. These are documented controls and processes, not proof that the service is best for every threat model. Certification scope and product scope must be checked separately. See OpenAI’s security and privacy page.
Google Gemini Apps
Google’s Gemini Apps Privacy Hub states that when Keep Activity is off, future chats do not appear in Activity and are not used to train AI models unless the user submits feedback. Google still keeps those chats for 72 hours to respond, process feedback, and protect Google, users, and the public.
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When Keep Activity is on, chats and shared content are saved and may be used to develop and improve services, including generative-AI training. The stated default auto-delete period is 18 months; users can choose three months, 36 months, or indefinite retention. Some chats are reviewed by human reviewers, including trained service providers. Chats reviewed to improve services or address safety may be retained for up to three years, disconnected from the user’s account, and are not deleted when Activity is deleted. Turning Keep Activity off or using temporary chats therefore reduces some improvement uses but does not disable all safety processing. Google advises against submitting confidential material unless the exact product terms and organizational safeguards are acceptable. See the Gemini Apps Privacy Hub.
Microsoft Copilot for personal accounts
Microsoft’s Copilot privacy-controls article applies to personal Microsoft-account Copilot and, following an August 18, 2026 update, describes the older app version. It provides an opt-out for using future conversation activity for model training and states an 18-month conversation-history default. The training control does not exclude other general product or system improvement, advertising, digital-safety, security, or compliance purposes. Do not apply these menu paths or terms to Microsoft 365 Copilot under a work or school account. See Microsoft’s Copilot privacy controls.
Microsoft’s individual Copilot Transparency Note, dated August 18, 2026, describes pre-release risk mapping and measurement, red teaming, and mitigations, and says the note is updated periodically. It covers personal-account Copilot; organizations need the documentation and terms for their own deployment. See the Transparency Note for Microsoft Copilot.
Microsoft 365 Copilot for organizations
Microsoft says prompts, responses, and Microsoft Graph data in Microsoft 365 Copilot are not used to train foundation large language models. Its organization documentation discusses stored interaction data, administrator controls, and regional processing. Regional routing can vary under high utilization, and Anthropic models as a subprocessor are currently excluded from the EU Data Boundary. That exception shows why a residency statement must be checked against the exact model and service path rather than accepted as a blanket guarantee. See Microsoft’s data, privacy, and security documentation.
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Anthropic’s Transparency Hub publishes model reports, capability-evaluation summaries, deployment safeguards, and risk assessments. A report page dated October 2, 2026 contains model-specific material. Treat each assessment as a provider-published account of the named model, configuration, methods, and date—not as an independent cross-provider ranking. See the Anthropic Transparency Hub.
NIST as a neutral risk-management frame
NIST released AI Risk Management Framework 1.0 on January 26, 2023. It is intended to be a living document and has a voluntary companion playbook that organizations can adapt. Use it to ask who is affected, what can go wrong, how risks are measured, which mitigations exist, and how the deployed system is monitored. It is a framework, not a vendor certification or ranking. See the NIST AI RMF FAQs.
Do AI providers use my chats to train their models?
Sometimes, and the answer can change by product, account, setting, feedback action, and date. “Not used for training” is one control, not a complete privacy guarantee. You still need to check service retention, safety review, abuse monitoring, feedback handling, connected-app data, legal holds, and other product-improvement uses.
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Ask these narrower questions:
- Does the statement cover prompts only, or also outputs, uploaded files, voice, feedback, and retrieved content?
- Is the setting an opt-out, an opt-in, or a business default that requires a qualifying plan?
- Does it apply to information already submitted, or only to future conversations?
- Can a thumbs-up, thumbs-down, bug report, or support ticket send the conversation into a separate review process?
Record the answer for the exact app or API. A consumer setting cannot be used to infer the behavior of an enterprise service, and an enterprise default cannot be used to infer the behavior of a personal account.
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Can AI companies read my conversations?
Potentially. Human reviewers or service providers may inspect selected content for safety, abuse prevention, quality improvement, troubleshooting, or feedback handling. The important questions are who is authorized, what triggers access, whether the account is separated from the review copy, how long the copy remains, and whether you can opt out.
Deletion also has boundaries. A visible chat can disappear from your history while a safety, legal, security, or de-identified copy remains. Google’s Gemini documentation, for example, says chats reviewed for service improvement or safety can remain for up to three years, disconnected from the account, and are not deleted when Activity is deleted. Treat every “delete” control as a statement about a defined data store, not proof that every copy disappears immediately.
How long does an AI chatbot keep my data?
Retention is a product rule, not a universal provider characteristic. Capture the default, the shortest configurable period, and every exception. Examples in the documented services include Google’s 72-hour processing period when Gemini Keep Activity is off, an 18-month default auto-delete period when it is on, and up to three years for chats selected for review. Microsoft’s personal Copilot documentation describes an 18-month conversation-history default. Business and API products may offer configurable retention or zero-data-retention eligibility, subject to endpoints and contract.
Ask whether retention covers backups, logs, abuse systems, reviewer tools, exports, and derived or de-identified records. If a policy does not state when a category is deleted, record it as an unresolved requirement and ask the provider in writing.
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It can provide stronger administrative and contractual controls, but “business” is not automatically private. Compare the actual plan and contract. For organizational or regulated use, review:
- Identity management, MFA or SSO, role separation, and least-privilege administration.
- Audit logs showing access, configuration changes, exports, and deletion events.
- Encryption in transit and at rest, customer-managed keys where offered, and key-rotation responsibilities.
- Retention configuration, backup treatment, legal holds, and verified deletion procedures.
- Data-processing locations, residency options, cross-border transfers, and subprocessors.
- A DPA, BAA, or equivalent commitment when personal or regulated data is involved.
- Incident notification, support access, suspension, export, and termination provisions.
Ask the privacy or security owner to approve the data-flow architecture and contract. A certification logo can support due diligence, but it is not blanket approval for every model, endpoint, region, or use case.
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How to judge safety and transparency evidence
Read safety disclosure as evidence of a process, not as a score. A useful report identifies the model and version, test date, configuration, threat scenarios, methods, evaluators, safeguards deployed, residual risks, and known limitations.
Separate evidence types:
- Provider testing: benchmarks, adversarial testing, red teaming, monitoring, and incident summaries produced or commissioned by the provider.
- Independent evaluation: work whose methods, evaluator, scope, and access are described well enough to assess independence.
- Operational controls: rate limits, abuse detection, human escalation, access separation, logging, and rollback or shutdown procedures.
- Recourse: channels to report harmful output, appeal enforcement, obtain support, and learn about material changes.
Do not turn one provider’s favorable report into a comparative safety percentage. The available documents do not establish a common cross-provider statistic. Microsoft’s Transparency Note states: “An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed.” That framing is useful because deployment context can change the consequences of the same model output.
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Microsoft’s 2026 responsible-AI report page says nearly 20,000 engineers, policymakers, and customers were trained in responsible AI. This is a reported training activity, not an outcome measure or a comparative privacy or safety score. See the Microsoft Responsible AI Transparency Report.
A practical seven-step selection process
- Inventory the data. List public information, routine business material, personal information, regulated records, credentials, and trade secrets. Identify the people and systems affected by exposure, retention, or a wrong answer.
- Choose the service context. Decide whether you need a personal chatbot, managed workspace, education or healthcare offering, or API. Record account type, plan, region, model, endpoints, and optional features.
- Collect current documents. Save the product privacy notice, retention and deletion documentation, terms, security or trust materials, subprocessor list, and any DPA or BAA. Record the date checked.
- Complete the comparison table. For every advertised control, write its scope, eligibility, default state, configuration path, and remaining exceptions. Use “not established” when the documentation is silent.
- Review organizational safeguards. For business or regulated workloads, involve privacy and security owners. Validate identity controls, auditability, encryption, data flows, residency, retention, deletion, subprocessors, and incident obligations.
- Examine dated safety material. Confirm the tested model and configuration, test date, methods, internal or independent evaluators, safeguards, limitations, incident channels, and update cadence.
- Run a low-risk pilot. Use synthetic or already-public data. Test deletion, exports, administrator visibility, feedback handling, logging, regional routing, abuse controls, and failure escalation before permitting real sensitive data.
Common mistakes and recovery actions
Comparing company names instead of products
Problem: A consumer app, business workspace, and API are treated as one service.
Recovery: Rebuild the comparison using product, plan, region, account type, model, and date as mandatory fields.
Stopping at “not used for training”
Problem: Retention, human review, safety processing, advertising, abuse monitoring, or feedback handling is overlooked.
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Assuming deletion erases every copy
Problem: Visible history is deleted, but safety, legal, backup, reviewer, or de-identified records remain.
Recovery: Ask which systems deletion covers, what exceptions apply, and when each category is purged or disconnected.
Treating a certification as a safety ranking
Problem: A SOC report, ISO certificate, or provider badge is interpreted as proof of suitability.
Recovery: Check the certificate’s service scope and pair it with model-specific testing, deployment controls, and your own threat-model review.
Ignoring model and routing changes
Problem: A residency or subprocessor promise is assumed to cover every model and traffic path.
Recovery: Verify supported endpoints, regional routing under high utilization, subprocessors, and any model-specific exclusions in the current organization documentation.
Decision rule: when not to choose a provider
Reject or postpone a deployment when a provider cannot document the controls required by your data classification. That includes unknown human-access rules, retention exceptions you cannot accept, unavailable contractual terms, unverified residency, inadequate administrator controls, or safety evidence that does not cover the model and use case.
Use a lower-risk workflow—such as synthetic data, redacted inputs, local processing, or a separately assessed deployment—until the missing requirement is resolved. Re-check documentation whenever the product, model, account, region, or contract changes.
Quick Recap
Final selection checklist
- Exact product, plan, account type, model, region, and review date are recorded.
- Data categories and potential harms are written down.
- Training and all other improvement uses are understood, including feedback.
- Default retention, configurable retention, deletion scope, and exceptions are documented.
- Human and service-provider access is explained.
- Security, administration, audit, encryption, and key-management controls fit the workload.
- Residency, routing, subprocessors, DPA or BAA, and incident terms are acceptable.
- Safety reports identify the tested model, date, methods, safeguards, and limitations.
- Users have reporting, appeal, export, and deletion paths.
- A low-risk pilot has verified the controls before sensitive data is permitted.
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