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You can evaluate a new AI model without exposing sensitive information: start with synthetic or public examples, and send real data only after confirming that the exact service, account, and endpoint are approved to handle it. Check training use, storage, retention, deletion, and access separately—“not used for training” does not necessarily mean “not stored.”
1. Design a test that does not need real sensitive data
First decide what you want to evaluate: instruction following, tone, code explanation, or summarization, for example. Build a small test around that capability using fictional names and synthetic details, or use material that is already public. Keep the test focused so you do not include extra context just because it is available.
If representative data is genuinely necessary, remove direct identifiers and confidential details. Then consider whether the remaining combination of facts could still identify a person or reveal protected information. Anonymization and synthetic data can reduce risk, but do not guarantee that a dataset is anonymous. AWS recommends these approaches for reducing exposure in regulated settings.
2. Get approval for the exact service and data
For work or regulated information, ask the data owner or your organization’s IT, privacy, or security contact whether the specific service, account or tenant, model endpoint, and intended use are approved. Approval to use a model is not blanket permission to send every dataset. The U.S. Department of Energy’s GEAR guidance puts it plainly: “Approval to access a model or agent does not mean every project dataset may be sent to that service.”
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Check the applicable service terms and settings for training use, retention, deletion, human or administrator access, and any required agreement. The UK Government’s guidance for UK public-sector users says not to enter official information into public AI applications or APIs unless it is already public or cleared for publication, and cautions against assuming default configurations are secure. That guidance applies to its stated public-sector context; your organization’s rules and jurisdiction may differ.
3. Check training, history, retention, and deletion separately
These are distinct questions: Are prompts used to improve models? Does the conversation appear in chat history? Is memory or personalization active? How long do service logs or application state persist? How does deletion work, and who can access the workspace? The answers vary by provider, product, account, and configuration.
Rank #2
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ChatGPT controls
OpenAI’s ChatGPT data-controls guidance, checked on October 7, 2026, says turning off “Improve the model for everyone” means new conversations will not be used to train OpenAI models; saved chats can still remain in chat history. Temporary Chats do not appear in history, do not create or update memories, and are not used to improve OpenAI models, but OpenAI says they may be retained for up to 30 days for safety purposes. That is a stated maximum, not a claim that every Temporary Chat is kept for the full period. Memory controls and model-training controls are separate. OpenAI’s ChatGPT data-controls guidance describes these service-specific settings; do not assume other providers work the same way.
OpenAI API controls
OpenAI says API inputs and outputs are not used to train or improve its models by default unless a customer explicitly opts in. Separately, abuse-monitoring logs may contain prompts, responses, and derived metadata, and are retained for up to 30 days by default. Modified Abuse Monitoring and Zero Data Retention require approval and have limitations; some features may still store application state. OpenAI states that the API training policy took effect on March 1, 2023. Check the current endpoint-specific data table and customer terms before relying on an exception. OpenAI’s platform data-controls documentation is specific to its API.
Rank #3
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4. Keep secrets and unnecessary context out
Do not include passwords, API keys, tokens, certificates, or connection strings in prompts, notebooks, screenshots, agent instructions, test fixtures, committed configuration, or logs. Remove personal and confidential details that are not necessary to the test. For work, follow the data owner’s rules and any applicable jurisdiction-specific requirements; no general model setting establishes that a particular dataset is legally permitted for a particular use.
If you need records of a test, keep only what is needed to reproduce it. Redact prompts and data in logs, and apply access and retention controls to those logs as well.
Rank #4
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5. Treat local execution as a boundary to check, not a guarantee
Running a model locally may avoid sending prompt text to a hosted inference endpoint, but it does not make the surrounding application automatically safe. Check whether the model or agent can reach files, environment variables, credentials, tools, retrieval indexes, logs, telemetry, plug-ins, or the network. Give it only the files and permissions needed for the test.
DOE GEAR specifically recommends limiting what a local development agent can read, including the parent process environment, home directory, credential files, and cloud metadata service. Privacy guidance also identifies prompt injection, model inversion, and jailbreaks as threats that need ongoing attention. Review model output before it triggers consequential actions.
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6. Compare your options using the same checks
There is no universally safest option established by these sources; safety depends on the data, provider, account, endpoint, and configuration. Compare a consumer hosted service, an approved enterprise or API service, and local execution against the same questions:
- Permission: Is this specific data allowed in this service and for this use?
- Training: Can prompts or outputs be used to train or improve models?
- Persistence: What history, logs, or application state is retained, for how long, and how can it be deleted?
- Access: Who can view prompts, outputs, and logs, including workspace administrators?
- Configuration: Are the exact tenant, endpoint, and account controls approved and enabled?
- Local boundaries: What files, secrets, tools, and network resources can a local model or agent access?
For highly sensitive information with no approved workflow, do not test it on an unapproved hosted service. Use synthetic or public material instead, or wait until the data owner approves a suitable path.
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