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Keep sensitive business data private by treating every generative-AI prompt, upload, connector, retrieval store, output and log as a governed data flow. Inventory the data, classify it, minimize and redact it before use, permit only reviewed tools, enforce least privilege and MFA, encrypt traffic and storage, set retention and deletion rules, monitor outputs, train employees and maintain an incident-response path.
No universal “private AI” setting makes these duties disappear. Whether a provider can train on your inputs, how long it retains them, where it processes them and who can access connected data depends on the exact product, plan, region and contract.
Why generative AI creates a business-privacy problem
A generative-AI request can send more information than the visible prompt. Depending on the product, the data flow may include uploaded documents, conversation history, system instructions, browser extensions, plug-ins, application programming interfaces, retrieval indexes, generated files, safety logs, administrator logs and human review. A seemingly harmless request to “summarize this contract” can therefore disclose the contract, its metadata and the resulting summary to several services.
The FTC’s business guidance gives a useful starting point: “TAKE STOCK. Know what personal information you have in your files and on your computers.” Apply that inventory to AI as well. Record what enters prompts and files, which systems receive it, where copies are stored, who can retrieve it and when each copy is deleted.
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Privacy is a business process rather than a promise that a model is inherently safe. NIST guidance calls for documented AI uses, privacy-risk assessments for personal information processed by AI systems and ongoing monitoring for exposure. Security, privacy, legal, compliance and records-management owners should therefore share one AI governance process.
Classify data before anyone uses an AI tool
Classification determines what an employee may do, which tool is approved and what additional controls are required. Publish definitions in plain language and attach examples from your own systems.
| Classification | Typical examples | Default generative-AI rule |
|---|---|---|
| Public | Published product documentation, press releases and information already released by the organization | May be used in an approved tool, subject to normal accuracy and copyright checks |
| Internal | Non-public procedures, draft plans and ordinary internal correspondence | Use only approved accounts with access controls; do not place it in consumer or unreviewed tools |
| Confidential | Customer records, source code, non-public financial information, employee data and unique contract terms | Redact or anonymize first; use only a vendor and configuration explicitly approved for this class |
| Regulated | Information governed by privacy, financial, health, employment, export-control or contractual rules | Require a documented legal and privacy assessment, regional and retention controls, and an approved processing agreement |
| Highly restricted | Credentials, private keys, authentication secrets, incident-investigation material, unreleased acquisition information and data whose disclosure could cause exceptional harm | Do not submit to a general-purpose model. Use a separately approved, tightly controlled workflow only when a documented business need exists |
Classification must follow the most sensitive item in a combined prompt. A document marked “internal” can still contain a customer identifier or secret that requires the stricter rule. Make the classification visible in document metadata and train staff to check the contents, not just the file name.
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The FTC describes the “SCALE DOWN” principle as keeping only what the business needs and retaining it only as long as necessary. The same principle applies before every AI request: send the smallest, least identifiable input that can produce a useful result.
Use a pre-prompt minimization check
- Write the business purpose in one sentence, such as “extract renewal dates from these agreements.”
- List the fields the model actually needs. If dates and clauses are enough, remove names, addresses, account numbers and unrelated pages.
- Replace direct identifiers with stable tokens such as
Customer_017. Keep the re-identification key in a separate, access-controlled system; never include that key in the prompt. - Remove secrets, passwords, API keys, private certificates, session tokens, payment-card data and database connection strings. Rotate a secret immediately if it was exposed.
- Crop attachments to relevant pages and strip comments, tracked changes, hidden sheets, embedded files and metadata that reveal people or systems.
- Check the destination, account, connector and sharing setting immediately before submission. An approved tool used through an unapproved extension is still an unapproved data flow.
What should never enter an ordinary chatbot
- Passwords, private keys, recovery codes, access tokens and other credentials
- Unredacted customer or patient records, government identifiers and complete employee files
- Material covered by a legal privilege, investigation hold or confidentiality clause unless counsel and the contract expressly permit the processing
- Detailed vulnerability information, production configuration, unreleased merger information and trade secrets
- Any information whose transfer, location or retention would violate a law, regulator requirement or customer contract
Redaction is not merely replacing visible names. Search for identifiers in tables, footnotes, image scans and formulas, and test that the model cannot infer the identity from a unique combination of facts. Keep an auditable record of the redaction method for regulated workflows.
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Approve tools and vendors as part of the security boundary
Before sensitive data flows to an AI service, complete a security, privacy, legal and procurement review. A vendor’s marketing description is not a retention policy. Obtain the exact plan terms, data-processing agreement and regional configuration that will apply to your users.
| Area to evaluate | Questions to document |
|---|---|
| Provider training use | Are prompts, uploads, outputs or feedback used to train or improve models? Is an opt-out available, enabled by default and enforceable by contract? |
| Retention and deletion | How long are content, abuse-review copies, backups and administrator logs retained? Can an administrator delete them, and how are legal holds handled? |
| Regional processing | Which countries process and store each data type? Can the organization select a region, and are support personnel allowed access from elsewhere? |
| Encryption and keys | Is data encrypted in transit and at rest? Are customer-managed keys, key rotation and separation of duties available? |
| Identity and audit | Does the service support SSO, MFA, role-based access, administrator separation, session controls and exportable audit logs? |
| Connectors and plug-ins | What repositories can an agent read or write? Are permissions inherited from the source system, and can each connector be disabled or scoped? |
| Incident handling | What is the notification deadline, investigation support, evidence access and process for deleting exposed data? |
| Assurance and change control | Which independent security reports or compliance attestations apply? How will the provider notify you about model, policy, subprocesser or jurisdiction changes? |
| Administrative prevention | Can administrators block file uploads, extensions, external sharing, high-risk actions and sensitive-data patterns? |
Do not describe an AI service as “enterprise private” without answering these questions for the actual edition. The cited official guidance establishes these as appropriate governance questions, but it does not establish universal retention or training terms for any particular commercial provider.
Put requirements in the contract
Specify permitted purposes, data categories, subprocessors, locations, confidentiality, security measures, retention and deletion, breach notification, audit rights, return of data and assistance with access or deletion requests. Require notice and a review period for material changes. A tool that is acceptable for public text may be prohibited for regulated records, even within the same company.
Control identity, access and connections
Least privilege limits the damage from a compromised account or an overly broad agent. Give each role only the repositories, models, actions and data classes required for its job. Separate ordinary users, developers, security reviewers and service administrators; use separate administrator accounts and review elevated access regularly.
- Require single sign-on where available and multi-factor authentication for every account that can reach business or customer information. FTC Safeguards Rule guidance specifically calls for MFA for covered financial institutions; other organizations should map the principle to their own laws and contracts.
- Use role-based groups rather than shared logins. Disable accounts promptly when a person changes roles or leaves.
- Keep retrieval indexes and vector stores under the same authorization model as the source documents. A model must not retrieve a file merely because someone can ask a clever question.
- Limit agent write actions. Start with read-only access, require confirmation for sending messages or changing records and block actions outside a documented allowlist.
- Review audit logs for unusual downloads, bulk prompts, privilege changes, connector additions and attempts to bypass policy.
Encrypt sensitive data in transit with TLS or an equivalent secure connection and encrypt retained data at rest. Where encryption is not feasible, document the approved compensating control. Protect encryption keys separately, restrict key administrators and test restoration without exposing plaintext.
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Set retention, deletion and legal-hold rules
Define separate retention periods for prompts, uploaded files, outputs, conversation history, safety-review copies, application logs and backups. A useful output is a retention schedule that names the owner, purpose, system of record, deletion trigger and evidence of deletion for each category.
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- Turn off optional history or training retention when the approved product supports that control, and verify that the setting applies to every workspace and API route.
- Provide a user-facing deletion process and an administrator process for bulk deletion, account closure and connector removal.
- Document legal holds so ordinary deletion does not destroy records that must be preserved.
- Test deletion, including cached copies, indexes, exports and backups. Record what the provider deletes directly and what remains until its normal backup cycle.
“If it’s not in your system, it can’t be stolen by hackers” is a practical FTC observation: reducing collection and retention reduces the material available to expose. It does not eliminate the need for access controls or incident response.
Secure connectors, extensions and retrieval systems
Connectors often create greater exposure than a single prompt because they allow repeated discovery across a repository. Inventory every browser extension, plug-in, API integration, shared drive, email connection and retrieval index that can send data to a model.
- Allow connections only from a centrally managed, approved list.
- Use a dedicated service identity with read-only permissions and a narrowly scoped folder or project.
- Prevent cross-tenant retrieval and ensure document-level permissions are enforced at query time, not copied once into a broad index.
- Block connectors from regulated or highly restricted repositories unless a specific assessment approves them.
- Log which user, connector, source document and action produced each retrieval or write.
- Revoke tokens and rebuild indexes when a connector is removed, a user loses access or a repository is reclassified.
Test for indirect leakage: ask whether a user can infer a confidential value through summaries, counts, error messages or comparisons even when the source file is not displayed.
Monitor outputs and logs for exposure
Models can reproduce sensitive text from an input, reveal information through an output, or invent a plausible but dangerous answer. NIST’s Generative AI Profile recommends: “Conduct periodic monitoring of AI-generated content for privacy risks; address any possible instances of PII or sensitive data exposure.” Make that action operational.
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A practical monitoring program
- Sample outputs by risk tier and inspect for personal identifiers, secrets, proprietary text, cross-customer content and unsafe summaries.
- Use automated detection for credentials, account numbers, government identifiers and known confidential terms, followed by human review for ambiguous matches.
- Alert on large uploads, repeated blocked attempts, unusual export volume, new connectors and prompts that combine restricted repositories.
- Keep enough prompt, identity and action metadata to investigate, but apply the same minimization and retention rules to those logs.
- Track false positives and false negatives, then adjust patterns, permissions and training rather than simply raising alert volume.
Output review does not replace human responsibility. Require a qualified person to verify legal, financial, safety, employment and customer-facing results before use, and record the source documents and approval where the decision is consequential.
Train employees and stop unsafe pasting
Recurring training is more effective than a one-time acceptable-use document. The FTC recommends security training and clear confidentiality expectations. Teach the behavior employees must follow:
- Which data classes are prohibited, restricted or allowed in each approved tool
- How to redact identifiers, secrets, hidden metadata and irrelevant pages
- How to recognize the organization’s approved account, region and connector
- Why browser extensions, personal accounts and copied conversation links can bypass controls
- How to verify generated content and prevent confidential text from entering a public answer
- How and where to report an accidental paste, upload, disclosure or suspicious output
Use technical controls alongside training: single sign-on, managed browsers, data-loss-prevention rules, upload blocking for restricted labels, extension allowlists and warnings that explain why a submission is blocked. Measure completion, run realistic exercises and retrain after policy or product changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose risk-based limits instead of an unmanaged ban
A blanket ban can push work into personal accounts that security teams cannot monitor. A risk-based model allows low-risk drafting and transformation while restricting high-risk data and actions.
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| Use case | Typical decision | Required conditions |
|---|---|---|
| Public text brainstorming or rewriting | Allow | Approved account, normal accuracy review and no confidential additions |
| Internal procedures or code assistance | Allow with controls | Reviewed tool, restricted repository, no secrets, access logging and human review |
| Confidential contract or customer-data analysis | Restricted | Redaction, approved regional processing, contractual protections, retention limits and privacy assessment |
| Regulated decisions or automated external actions | Case-by-case approval | Legal review, documented risk assessment, explainability and a human decision owner |
| Credentials, keys or highly restricted investigations | Prohibit in general-purpose tools | Only a separately approved environment with exceptional controls and documented need |
Federal provisions referenced in the guidance favor risk-based limits, appropriate vendor terms and controls for confidentiality, privacy, records and data protection. Apply that approach to your sector rather than assuming one rule fits every organization.
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Prepare an incident-response path
Assume someone will eventually paste or upload the wrong material. Make the first actions obvious and non-punitive so reporting is fast.
- Contain: stop the conversation, revoke shared links and connector tokens, disable the account or integration if necessary, and prevent further propagation.
- Preserve evidence: record the user, time, data categories, recipients, prompts, outputs, settings and relevant logs without creating extra copies of the sensitive material.
- Notify internally: contact the security incident owner, privacy officer, legal counsel and the business data owner under the written escalation plan.
- Engage the provider: request preservation, deletion, access logs and confirmation of downstream recipients under the contract’s incident process.
- Assess obligations: determine whether customer, employee, regulator, insurer or law-enforcement notification is required in each affected jurisdiction and contract.
- Remediate: rotate exposed secrets, correct permissions, remove the connector, update detection rules and retrain the affected group.
- Learn: document the root cause and control change, then test that the same path is blocked.
A practical implementation sequence
First 30 days: establish visibility
- Name an owner and cross-functional steering group.
- Inventory AI tools, accounts, extensions, connectors, models, data stores and current retention settings.
- Publish the five data classes and a short prohibited-data list.
- Freeze new sensitive-data integrations until review.
Days 31–60: deploy minimum controls
- Move approved users to managed accounts with SSO, MFA and role-based groups.
- Enable encryption, logging, upload restrictions and extension allowlists.
- Set retention and deletion schedules, including legal holds.
- Complete vendor contracts and privacy-risk assessments for high-risk uses.
- Deliver role-specific training and an easy incident-reporting route.
Days 61–90: test and improve
- Run redaction, permission, retrieval and deletion tests using realistic but non-production data.
- Review sampled outputs for personal or proprietary-data leakage.
- Exercise the incident-response plan with the provider and legal team.
- Reassess vendors and models after material product, policy, model or jurisdiction changes.
- Report measurable findings to leadership: blocked submissions, unresolved high-risk access, deletion-test results, training completion and open incidents.
How to answer the common privacy questions
Can a provider train on company data?
There is no universal answer. Training or improvement use, opt-out behavior, abuse-review access and retention vary by product, plan, region and contract. Verify the exact terms and test the setting; do not rely on a general statement about the provider.
Is enterprise AI private?
An enterprise label is not a control by itself. Privacy depends on identity, permissions, connectors, encryption, regional processing, retention, deletion, logging, contractual limits and monitoring configured for your deployment.
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Ban prohibited data and unsafe tools, but permit low-risk uses through managed accounts and clear limits. A risk-based program is more enforceable and more visible than an unmanaged prohibition.
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