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Do not treat an AI assistant as a private notebook. Protect confidential business data by deciding what may be entered, using organization-managed accounts, minimizing and redacting prompts, restricting connected data, configuring retention and monitoring, and requiring human review. Enterprise assurances reduce particular risks; they do not make every prompt, connector, web search, administrator action, or stored log risk-free.
The practical test is authorization: Is this employee, this account, this assistant, and this integration explicitly approved to process this classification of data in this jurisdiction? If the answer is unclear, keep the original data out and use a redacted summary or synthetic example.
Why confidential data needs special handling in an AI assistant
An assistant can receive your prompt, attached files, conversation history, connected application data, and sometimes a web-search query generated from your request. It may return an answer that exposes more surrounding context than you intended, or it may produce an incorrect recommendation that someone treats as authoritative. The risk is therefore broader than model training.
- Disclosure: A prompt, uploaded file, response, search query, support ticket, abuse-monitoring record, or administrator export may become a stored business record.
- Unauthorized retrieval: A connected assistant can use the permissions of a file repository or service. Overshared source files remain overshared when an AI search layer is added.
- Inference: Names, dates, transaction patterns, and partial identifiers can reveal a customer, deal, investigation, or strategy even when a user omits an obvious secret.
- Integrity: An answer can be wrong, incomplete, or manipulated by malicious instructions in a document or webpage.
- Regulatory exposure: Personal, health, financial, export-controlled, privileged, or contractual data may have handling, location, deletion, or processor requirements that a general-purpose chatbot does not satisfy by default.
National Institute of Standards and Technology (NIST) Special Publication 1800-28, published in February 2024, frames confidentiality as an asset-protection problem: identify valuable data, understand threats, and apply controls that prevent or limit breaches. It is guidance, not a certification or a universal “safe to paste” threshold.
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Classify data before anyone opens a chat window
A short classification scheme gives employees a decision they can make quickly. Map these labels to your existing information-security policy rather than creating a parallel vocabulary.
Public
Information already approved for public release, such as a published product page or press announcement. Public material can usually be used in an approved assistant, subject to copyright and accuracy review.
Internal
Routine operating information intended for staff, such as a non-sensitive meeting agenda or a generic process description. Use a managed account and avoid combining internal material with personal data or secrets.
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Information whose disclosure could harm the company or another party: customer identifiers, non-public pricing, contracts, source code, incident details, unreleased strategy, employee records, and material received under a confidentiality agreement. Require an approved use case, approved account, and appropriate controls before processing.
Restricted or regulated
Credentials, private keys, payment data, health information, legal privilege, trade secrets, export-controlled material, and data subject to sector or contractual rules belong in the highest-control category. Default to prohibition unless security, privacy, legal, and the data owner have approved a specific workflow.
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Unless a documented exception exists, block or redact the following before submission:
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- Passwords, API keys, access tokens, private cryptographic keys, recovery codes, and connection strings.
- Full customer, patient, employee, or applicant records; government identifiers; payment-card details; and unnecessary contact information.
- Unannounced acquisitions, financing, earnings, product launches, vulnerabilities, breach investigations, or negotiation positions.
- Attorney-client or other privileged advice, whistleblower reports, protected investigations, and confidential board material.
- Source code or configuration that contains secrets, proprietary algorithms, or security weaknesses not approved for external processing.
- Documents whose contracts prohibit disclosure to an additional processor or restrict geography, retention, or onward transfer.
- Any file or prompt that the employee is not authorized to access or share. An AI interface does not expand the employee’s underlying authorization.
If a task appears to require one of these items, stop and use the organization’s security or privacy review path. Do not assume that deleting the chat later removes copies from logs, backups, exports, or a connected system.
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A policy should state what is allowed, by whom, in which account, with what data, and under which review requirement. “Use AI responsibly” is not an enforceable control.
Define permitted uses
Common lower-risk examples include drafting from public text, transforming a redacted internal procedure, generating test data, brainstorming with synthetic examples, and summarizing a document after sensitive fields have been removed. Specify whether the output may enter production systems and who must check it.
Define prohibited and review-required uses
Prohibit secrets and unapproved regulated data. Require security and privacy review for confidential or restricted material, external-facing automated decisions, high-impact employment or lending decisions, legal advice, and connectors that can search company repositories.
Assign accountability
- Data owner: decides whether a classification may be used for a particular purpose.
- Security and privacy: evaluate identity, logging, retention, encryption, geography, incident handling, and processor terms.
- IT or platform team: configures tenants, groups, connectors, DLP, and audit access.
- Employee: uses the approved account, minimizes data, checks permissions, validates the result, and reports mistakes.
Make the rule visible at the point of use
Put a short warning in the chat portal and in onboarding: do not enter passwords, keys, unnecessary personal data, or unapproved confidential information. Add examples of acceptable redaction, a link to the approval process, and a one-click route to report accidental disclosure.
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Account tier matters. Consumer and organization-managed products can have different training, retention, administrative, identity, and contractual terms. A personal account also prevents the company from reliably enforcing single sign-on, multifactor authentication, role-based access, DLP, retention, audit, and departure-offboarding controls.
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Use an organization-managed tenant or API project with a contract that identifies the service provider, processing purposes, subprocessors, geography, deletion process, breach-notification duties, and regulated-data terms. Require SSO and MFA, disable personal accounts for corporate work where technically possible, and remove access promptly when a worker changes role or leaves.
Understand the difference between model training and storage
A no-training promise answers one question: whether submitted data is used to train or improve foundation models under the stated terms. It does not mean that no copy is stored. Prompts and responses can still appear in service logs, abuse-monitoring records, conversation history, backups, administrator views, eDiscovery exports, support cases, or third-party connectors.
What current vendor statements establish
| Service or account context | Training statement | Logging, retention, or security detail |
|---|---|---|
| OpenAI business products and API | OpenAI says data from ChatGPT Enterprise, ChatGPT Business, ChatGPT Edu, ChatGPT for Healthcare, ChatGPT for Teachers, and its API platform—including inputs and outputs—is not used by default to train or improve models. | OpenAI describes encryption at rest and in transit, retention controls, and optional enterprise key management. API abuse-monitoring logs may contain prompts and responses and are retained up to 30 days by default. |
| Microsoft Copilot with managed work or school accounts | Microsoft states that prompts, work content added to prompts, and Copilot responses are not used to train foundation models. | Prompts, Bing queries triggered by prompts, and responses in Copilot Chat are logged. Web-search handling is separate from the handling of Microsoft Graph content. |
| Google Workspace Gemini | Google says Workspace prompts, Workspace content, webpage context, and generated responses are not used to train models without permission. | Gemini applies existing Workspace security and data-handling practices. Client-side encryption can restrict access to sensitive content. |
| Consumer AI accounts | Terms and controls vary by product, plan, region, and setting; verify the current consumer policy before using business information. | Do not infer enterprise retention, administrator, or contractual controls from a consumer interface. |
These statements are scoped claims, not blanket privacy guarantees. They may not cover every connector, web-search provider, feedback channel, support workflow, administrator export, or retention store. Recheck the current service terms and your contract when a workflow changes.
Apply the control sequence in this order
- Inventory and classify. List the data sources employees want to use, their owners, classification, legal constraints, and required deletion period.
- Approve use cases. Record the task, users, account or API project, allowed fields, output destination, human reviewer, and escalation contact. Require a security and privacy review for restricted data.
- Choose a managed service. Require SSO, MFA, RBAC, administrative separation, audit logs, retention settings, and contract terms before enabling production use.
- Minimize the prompt. Replace names with labels such as
Customer-A, remove exact dates and identifiers where they are not needed, summarize long documents, and use synthetic examples for testing. - Inspect attachments and context. Remove hidden spreadsheet tabs, comments, tracked changes, metadata, embedded credentials, and unrelated pages. Check what the assistant will retrieve automatically.
- Restrict connectors. Enable only the repositories and actions required for the approved use case. Correct overshared source files and inherited permissions before turning on retrieval.
- Set retention and geography. Configure conversation deletion, API log handling, backup periods, regional processing, encryption, and customer-managed keys where offered and required.
- Monitor and train. Review usage, blocked events, unusual downloads, connector access, and repeated attempts to submit secrets. Teach staff to recognize prompt injection and data-exfiltration requests.
- Validate and record. Require a named human to check factual accuracy, citations, calculations, confidentiality, and the destination of the output. Preserve the review record when the result affects customers, employees, finances, safety, or legal obligations.
- Exercise incident response. Define how to revoke credentials, stop a connector, preserve logs, notify the data owner, assess affected individuals, and meet contractual or regulatory deadlines.
Redact and minimize without destroying usefulness
Use placeholders consistently
Replace a person, customer, or deal with a stable token such as Employee-17 or Project-Orion. Keep the mapping outside the assistant. If the task needs relative timing, use “day 1” and “day 14” instead of dates that identify a real investigation.
Send only the fields needed for the task
For a policy summary, provide the relevant clauses, not the full contract and signature page. For a SQL explanation, provide a schema with fictional values, not a production export. For a support draft, include the error and product version while omitting the customer’s name, email, account number, and authentication data.
Check transformations for reversibility
Simple masking can fail when rare combinations, formatting, or surrounding context identify a person. Aggregate small groups, generalize locations, remove free-text notes, and test whether a colleague could reconstruct the original record from the transformed prompt.
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Secure connected files, applications, and retrieval
Retrieval does not fix an authorization problem. If a repository allows broad access, an assistant may make that information easier to discover and combine. Review both the user’s permission and the service account’s permission.
- Remove public links and inherited access that no longer has a business reason.
- Separate confidential projects into repositories with narrow groups and distinct retention rules.
- Use read-only connectors unless an action is essential; require confirmation for sending mail, changing records, publishing content, or executing code.
- Disable connectors that are not part of an approved use case, including personal storage, unmanaged browsers, and consumer plug-ins.
- Test retrieval with users from different roles. Confirm that a junior user cannot obtain executive, legal, HR, or security material through a natural-language query.
- Inspect the assistant’s cited sources and retrieved snippets. A response that looks harmless may reveal a confidential file title, author, amount, or project name.
Configure identity, DLP, retention, and audit controls
Identity and least privilege
Use SSO with MFA, group-based RBAC, separate administrator accounts, and just-in-time elevation for sensitive configuration. Give a connector the smallest repository scope and action set possible. Review service accounts, tokens, and OAuth grants on a schedule.
DLP and sensitivity labels
Block or warn on patterns such as API keys, payment-card numbers, government identifiers, and protected health information. Apply sensitivity labels to files and prevent a high-sensitivity document from being copied into an unapproved assistant. Tune rules with test data so that employees can complete legitimate low-risk work without learning to bypass controls.
Retention and deletion
Set a business-justified conversation and log period, then document what deletion means for active storage, backups, abuse monitoring, exports, and legal holds. API customers should distinguish application logs from provider abuse-monitoring logs; OpenAI’s current API documentation describes abuse-monitoring retention of up to 30 days by default. A shorter internal chat-retention setting does not automatically erase provider or backup records.
Encryption and key ownership
Require encryption in transit and at rest. For restricted workloads, evaluate customer-managed or enterprise key options, key rotation, separation of duties, and what happens when a key is disabled. Encryption protects stored data from some threats; it does not stop an authorized administrator, connector, or user from viewing content.
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Audit and eDiscovery
Capture sign-in events, prompts and responses where contract and law permit, file retrieval, connector changes, administrator actions, DLP matches, exports, and deletion requests. Restrict audit access, protect logs from alteration, and define how legal holds interact with ordinary deletion.
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Handle web search and prompt injection as separate risks
An assistant may send part of a prompt to a web-search service or use webpage content supplied by a search result. Microsoft states that Bing queries triggered by Copilot prompts have separate handling, while prompts and responses are logged. Treat a web-enabled assistant as a distinct data flow: decide what text may leave the tenant, whether URLs or query terms reveal a project, and whether the result is allowed to influence an automated action.
Prompt injection is an instruction hidden in a document, webpage, email, or code comment that attempts to make the assistant reveal data or take an action. Train users and developers to treat retrieved text as untrusted input. Keep secrets out of the model context, use allow-listed tools, require explicit confirmation for external actions, and test with hostile documents before deployment.
Review every output before it leaves the organization
Human review must check more than spelling. The reviewer should verify:
- facts, calculations, dates, quotations, and references against authoritative records;
- that no customer, employee, source-code, or internal-project detail was unnecessarily reproduced;
- that the answer did not rely on an unauthorized connector or web result;
- that legal, medical, financial, security, and compliance conclusions went to the qualified person responsible for them;
- that the output is labeled as AI-assisted when policy, contract, or law requires disclosure; and
- that the final recipient and storage location have the correct access controls and retention period.
Compare an assistant before approving it
Use the following checklist with the vendor, your contract, and a controlled technical test. A marketing statement is not a substitute for a signed term or an observed administrative control.
| Evaluation area | Questions to answer |
|---|---|
| Managed account and contract | Is there an organization tenant, data-processing agreement, named subprocessors, support boundary, and breach-notification process? |
| Training use | Are prompts, files, outputs, feedback, and retrieved content excluded from foundation-model training by default, and are exceptions clear? |
| Service and abuse logs | What is logged, for what purpose, for how long, and who can access it? |
| Retention and deletion | Can administrators set retention, delete conversations, handle backups and legal holds, and verify completion? |
| Encryption and key ownership | Are data in transit and at rest encrypted? Are customer-managed keys, rotation, revocation, and regional key storage available? |
| Identity | Are SSO, MFA, RBAC, SCIM or equivalent provisioning, session controls, and administrator separation supported? |
| Connected-data permissions | Does retrieval honor source permissions at query time? Can connectors be scoped read-only and limited by group or repository? |
| DLP and sensitivity labels | Can the organization block secrets and regulated data, apply labels, and receive actionable policy events? |
| Audit and eDiscovery | Are prompt, response, retrieval, export, connector, and administrator events available with appropriate access controls? |
| Geographic boundary | Where are prompts, files, logs, support records, backups, and keys processed and stored? |
| Web-search handling | Can web search be disabled or isolated? What query text and URLs leave the managed environment? |
| Incident response | What notification time, evidence access, containment support, and customer communication are included? |
| Regulated-data addenda | Are sector-specific terms, approved regions, and required technical safeguards available for the intended data? |
What to do after an accidental disclosure
- Stop further exposure: end the session, disable the connector or shared link, and revoke or rotate any credential that appeared in the prompt.
- Preserve evidence: record the account, time, conversation or API request, files involved, recipients, settings, and relevant audit events without copying the sensitive content into another unapproved system.
- Notify the right owners: contact security, privacy, legal, and the data owner using the incident channel. Do not quietly delete evidence or ask the assistant to “forget” it as a substitute for response.
- Assess scope: determine whether data was stored, retrieved, exported, sent to web search, viewed by another user, or included in a response delivered externally.
- Contain and remediate: correct repository permissions, tighten DLP rules, remove unnecessary access, update the policy, and retrain the people involved.
- Meet obligations: follow contractual, employment, sector, and jurisdictional notification requirements. ICO AI and data-protection guidance emphasizes assessing how AI processes personal data and implementing compliant governance; it does not create a universal permission to paste personal data.
A practical approval decision
Approve a use case only when all five answers are positive:
- The user is authorized to access and share the data.
- The classification is permitted for this assistant, account, region, and purpose.
- The prompt contains the minimum necessary, redacted information.
- Connected sources, retention, identity, DLP, logging, and deletion match the risk.
- A named human will validate the output and an incident path exists.
If any answer is negative or unknown, downgrade the data: remove identifiers, use a synthetic example, summarize the relevant rule, or obtain formal approval before proceeding.
Resources for a formal program
NIST SP 1800-28, “Data Confidentiality: Detect, Respond to, and Recover from Data Breaches,” provides a practical framework for identifying data assets and defending against confidentiality attacks. Use it as implementation guidance, not as a certification or a guarantee that a particular AI product is safe.
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