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For private or proprietary code, the safest choice is usually not a model name but a correctly configured account tier. OpenAI says data from ChatGPT Business, Enterprise, Edu, Healthcare, Teachers and the API is not used for model training by default. Anthropic separates consumer Claude settings from its commercial and API terms: consumer chats or coding sessions can enter model-improvement pipelines when you allow that setting, while Incognito chats are excluded and commercial/API arrangements can provide stronger contractual controls.
OpenAI also publishes the more extensive set of publicly documented sign-in controls found in this comparison: multifactor authentication (MFA), passkeys, FIDO-compatible security keys, recovery keys, active-session review and Advanced Account Security. That does not prove Claude is insecure; Anthropic simply does not expose an equivalent public consumer control page in the material available for this comparison.
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The short answer: choose the tier and settings before you choose the assistant
If a code sample contains credentials, customer data, private certificates or unreleased intellectual property, do not paste it into an ordinary consumer chat and assume that a model’s brand name makes it private. Use a business or enterprise workspace, or an API contract with explicit data-use terms; select the shortest retention or zero-data-retention option available; remove secrets before submission; and protect the account with phishing-resistant authentication.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteOn the evidence published by the companies on 20 October 2026, OpenAI is easier to audit for account security because its help documentation describes more controls in one place. Privacy is more conditional:
#1 Best Overall
| Question | ChatGPT/OpenAI | Claude/Anthropic |
|---|---|---|
| Is submitted code used for training? | OpenAI says inputs and outputs from ChatGPT Enterprise, Business, Edu, Healthcare, Teachers and the API are not used to train or improve models by default. | Consumer chats and coding sessions can be used for improvement if you enable Model Improvement. Anthropic says Incognito chats are excluded even when that setting is enabled. Commercial/API terms are separate. |
| How long can data remain? | Deleted GPT, project or file data is scheduled for permanent deletion within 30 days, subject to security or legal exceptions. | When consumer model improvement is allowed, Anthropic says de-identified chats or coding sessions may be retained for up to five years in training pipelines. API arrangements can include zero-data-retention terms. |
| Can you use stronger authentication? | Publicly documented options include MFA, passkeys, FIDO-compatible security keys, recovery keys, active-session review and Advanced Account Security. | Anthropic’s public material reviewed here did not expose an equivalent consumer MFA/passkey control page. Do not interpret that documentation gap as proof that no controls exist. |
Those are policy and product commitments, not an independent measurement of breach frequency or vulnerability rates. No reliable public statistic establishes that one model is intrinsically “the safest.”
What “privacy” means for a coding assistant
Three separate questions are often collapsed into one:
- Training use: may the provider use your prompt, source code or generated output to improve a model?
- Operational retention: how long can the service or an administrator retain the conversation, logs, files and abuse-monitoring records?
- Access and governance: which staff, workspace administrators, integrations or legal processes can access the material?
A no-training promise addresses the first question only. A service can refrain from training on your code while retaining it for service delivery, security, billing, abuse prevention or legal obligations. Conversely, a short retention period does not help if a secret is exposed to a model-improvement pipeline before deletion. Ask for all three answers in the plan and contract you are actually using.
ChatGPT privacy by account type
Business, Enterprise and other organizational products
OpenAI’s Business data privacy, security, and compliance statement says: “By default, we do not use data from ChatGPT Enterprise, ChatGPT Business, ChatGPT Edu, ChatGPT for Healthcare, ChatGPT for Teachers, or our API platform—including inputs or outputs—for training or improving our models.” The wording matters: it covers both what you send and what the system returns, and it is a default for the named products rather than a universal promise for every ChatGPT account.
Rank #2
Organizational plans also let a company set its own governance around membership, access and retention. Exact administrative features depend on the product and contract, so confirm the current workspace settings rather than relying on the word “Business” in a reseller description or an old screenshot.
Consumer ChatGPT accounts
A personal account is not covered by the organizational default above. Before submitting proprietary code, open the current Data Controls and privacy settings for that account and verify whether model-improvement use is enabled. The setting can change the treatment of future conversations; it does not retroactively make previously shared secrets safe.
Deletion is not instant erasure from every system
OpenAI’s deletion guidance says deleted GPT, project or file data is scheduled for permanent deletion within 30 days unless security or legal obligations require longer retention. “Scheduled” and “subject to exceptions” are important qualifications. Deleting a conversation or file is good hygiene, but it is not a substitute for avoiding secrets in the first place or for selecting an organizational/API arrangement with suitable retention terms.
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Claude privacy by account type
Consumer Claude and Model Improvement
Anthropic’s Privacy Center says that if you allow the company to use your chats or coding sessions to improve Claude, it may retain that data in a de-identified format for up to five years in model-training pipelines. This is a configurable consumer privacy choice, not a statement that every Claude conversation is kept for that period.
Check the Privacy Settings page before coding. If your organization prohibits training use, leave Model Improvement disabled and document that setting for the team. A workspace policy should also address browser extensions, shared accounts and exports, which can create copies outside the original chat history.
Incognito chats
Anthropic states: “Your Incognito chats are not used to improve Claude, even if you have enabled Model Improvement in your Privacy Settings.” Incognito is therefore a useful separation for a sensitive, one-off question, but it does not remove the need to redact credentials or to understand operational retention, administrator access and legal exceptions.
Commercial and API arrangements
Anthropic’s API documentation describes zero-data-retention arrangements and says retained data is never used for model training without express permission. “Zero data retention” can depend on an approved arrangement, endpoint and abuse-monitoring terms; it should be confirmed in the current contract and technical documentation rather than inferred from an ordinary consumer subscription. Ask whether your exact API traffic, logs, uploaded files and support tickets are covered.
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| Control | Why it matters to a developer | What to verify |
|---|---|---|
| Model-improvement switch | Determines whether conversations or coding sessions may contribute to improvement workflows. | Is it on by default? Does changing it affect only future chats? Can an administrator enforce it? |
| Temporary or Incognito mode | Creates a separate path for sensitive, short-lived work. | Whether those chats are excluded from training, how long they remain in operational logs and whether files are treated the same way. |
| Workspace/API no-training term | Provides a stronger contractual boundary for proprietary code. | The exact product, region, endpoints and whether inputs, outputs, files and support data are included. |
| Retention period | Limits the window in which an account compromise or internal misuse can expose history. | Default and maximum periods, backups, abuse logs, legal holds and administrator exports. |
| Deletion process | Determines what happens after a user removes a chat, project or file. | Whether deletion is scheduled, the stated time frame and security/legal exceptions. |
Keep an internal record of the setting, date, workspace and contract version. Privacy controls are volatile; a screenshot or policy PDF gives your security review something auditable to revisit.
Rank #4
Account security: where OpenAI is more clearly documented
MFA and phishing-resistant sign-in
OpenAI documents ordinary MFA as well as passkeys and FIDO-compatible security keys. Its help guidance states: “You can use any FIDO-compatible security key with Advanced Account Security, and you can also use passkeys.” A hardware key or passkey resists phishing better than a one-time code typed into a fake sign-in page. Use a password manager for a unique account password, then register at least two authenticators so losing one does not lock out the team.
Recovery and active sessions
Advanced Account Security includes recovery-key handling and lets you review active sessions. Store recovery keys offline in a controlled location; placing the only copy in the same cloud account defeats the recovery plan.
Enabling MFA does not automatically terminate existing logins. OpenAI explicitly warns: “Enabling MFA does not automatically log you out of other devices.” If compromise is suspected, reset the password and use the account’s session-management or logout-all-devices control as a separate incident step, then rotate any API keys that may have been exposed.
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The available Anthropic material gives strong evidence about consumer privacy settings and API retention arrangements but does not provide an equivalent public consumer page documenting MFA, passkeys, recovery keys and active-session review together. That supports the narrower conclusion that OpenAI’s controls are more extensively documented publicly—not that Claude lacks secure authentication or that an Anthropic enterprise contract cannot provide administrative controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set up a safer ChatGPT coding workflow
- Identify the data class. Mark the repository as public, internal, confidential, regulated or containing credentials. Do not treat a code snippet as harmless merely because it is short.
- Select the right product. For proprietary work, use an organization plan or API arrangement whose no-training terms cover your traffic. Record the exact workspace and region.
- Review Data Controls. Confirm model-improvement behavior for the account and check whether administrators can enforce the setting.
- Enable Advanced Account Security. Turn on MFA, then prefer a passkey or FIDO-compatible key. Register a backup authenticator and save recovery keys offline.
- Inspect sessions. Review active devices after enabling MFA. End sessions you do not recognize; MFA alone leaves existing sessions active.
- Redact before sending. Remove API keys, passwords, bearer tokens, private certificates, customer records, production URLs and identifying comments. Replace them with clearly labeled placeholders inside the code.
- Constrain the prompt. Send the smallest function or error trace that answers the question. State the language, runtime and sanitized inputs instead of uploading the entire repository.
- Clean up. Delete uploaded files and chats when they are no longer needed, while remembering that deletion follows the provider’s stated schedule and exceptions.
Set up a safer Claude coding workflow
- Choose consumer, team or API deliberately. If the code is proprietary, prefer a commercial workspace or API terms that explicitly address training and retention.
- Audit Model Improvement. In a consumer account, verify whether improvement use is enabled. Disable it when policy requires no training use.
- Use Incognito for isolated sensitive questions. Anthropic says Incognito chats are not used to improve Claude, even when Model Improvement is enabled. Still redact secrets and verify operational retention for your environment.
- Confirm API retention in writing. If using zero-data-retention processing, ensure the arrangement covers the endpoint, logs, files and any abuse-monitoring records your application generates.
- Apply organization access controls. Restrict membership, use single sign-on or the strongest available authentication, and remove departing users promptly. Because public consumer documentation is less explicit, obtain the control list from Anthropic or your contract for a security review.
- Minimize the payload. Send a reproducible fragment with synthetic data, not a production dump or a whole repository.
Decision guide for common coding situations
| Situation | Recommended approach | Reason |
|---|---|---|
| Learning with public or synthetic code | Either consumer service; still use a unique password and MFA. | The confidentiality impact is low, so convenience can outweigh enterprise administration. |
| Internal code with no credentials | Business/team workspace with model-improvement controls disabled or covered by contract. | You need organizational access management and a documented data-use boundary. |
| Customer data, regulated records or production secrets | Do not paste raw material. Use a reviewed API or enterprise arrangement, redact or synthesize data, and obtain legal/security approval. | Neither a consumer privacy toggle nor deletion after the fact is an adequate control for high-impact data. |
| One-off sensitive debugging question | Use a temporary/Incognito path where available, after removing secrets. | It reduces model-improvement exposure but does not eliminate operational or account risks. |
| Automated repository integration | Use an API with least-privilege keys, rotation, network controls and explicit retention terms. | Automation creates durable logs and credentials that require lifecycle management beyond a chat UI. |
Failure modes and incident response
You pasted a token or private key
Revoke or rotate the credential immediately at its issuing system; do not wait for chat deletion. Check access logs, notify the owner of the affected system and then remove the text from the assistant conversation. A deleted message cannot un-expose a credential that was already copied or used.
You enabled MFA after a suspicious login
Change the password, terminate existing sessions, rotate API keys and review account activity. OpenAI’s documented behavior means MFA activation by itself does not perform those steps.
Your team cannot prove which setting was active
Pause sensitive uploads, capture the current privacy and retention configuration, identify the account tier and ask the provider for the governing terms. Treat unknown provenance as a security gap rather than assuming the most favorable policy.
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A vendor promises “private AI” without naming the product
Request the exact plan, endpoint, region, training-use language, retention schedule, subprocessors, administrator access model and deletion exceptions. Plan names and marketing labels are not evidence by themselves.
Questions to settle before approving either service
- Are inputs, outputs, uploaded files, generated patches and support tickets all covered by the no-training promise?
- Can an administrator enforce model-improvement and retention settings for every user?
- What is retained in backups, abuse-monitoring systems and legal holds?
- Can users export or share a conversation, and can those copies be revoked?
- Which authentication methods, recovery paths and session controls are documented for the exact edition?
- How are API keys issued, scoped, rotated and revoked?
Bottom line
For ordinary consumer use, Claude’s Incognito and model-improvement controls can reduce training exposure, while ChatGPT offers a clearly documented set of phishing-resistant account defenses. For proprietary coding, neither consumer default is the right baseline: choose a business, enterprise or API arrangement with explicit no-training language, configure minimal retention, redact secrets and enforce strong identity controls. OpenAI is currently easier to verify from public account-security documentation; Anthropic can be appropriate when its commercial or API terms meet your organization’s privacy and retention requirements.
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