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Keep low-risk experimentation available where controls make it safe. A risk-based policy is easier to follow and easier to enforce than an absolute prohibition that drives work into unsanctioned channels.
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Why a public AI prompt is an external disclosure
When an employee pastes a customer record, source-code fragment, contract, credential, or internal plan into a public chatbot, the organization has transmitted that information to an external service. Whether the provider uses the prompt for model training is only one question; the data may also be retained in conversation history, visible to provider personnel under limited circumstances, transferred between regions, exposed through a compromised account, or included in logs and backups.
NIST’s NCCoE Special Publication 1800-28 (2024) treats confidentiality breaches as “unauthorized exfiltration, leaking, or spills of data to unauthorized parties, including the general public.” A public AI service is therefore an external disclosure destination unless your organization has verified contractual, technical, and operational controls that make the service an approved processor for the particular data.
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Do not rely on an employee’s judgment that a prompt is harmless because it is short, anonymized, or “only used for formatting.” A few fields can identify a person when combined, and code or business language can reveal more than its author realizes. Treat every prompt, uploaded file, image, and pasted conversation as a potential data transfer.
Start with a data-classification rule employees can apply
Employees need a short decision they can make before opening a tool. Use the organization’s existing classification scheme or establish five practical classes. Put examples beside each class in the policy and in the prompt interface so the rule is visible at the moment of use.
| Class | Typical contents | Public AI treatment |
|---|---|---|
| Public | Published web copy, public product documentation, press releases, public statistics | May be used in public tools if no hidden context is added. |
| Internal | Routine procedures, non-public meeting notes, internal FAQs without personal or regulated data | Use an approved workspace unless the information owner has explicitly allowed public use. |
| Confidential | Customer or vendor information, pricing, contracts, source code, security findings, unreleased results | Prohibited in public tools; use an approved enterprise environment with access controls. |
| Regulated | Personally identifiable information (PII), protected health information (PHI), payment data, records covered by sector or privacy law | Prohibited in public tools. Process only through an authorized, documented workflow. |
| Restricted | Credentials, private keys, incident evidence, legal advice, merger plans, pre-decisional policy, material that could cause severe harm if disclosed | Prohibited in public tools and subject to the strongest internal controls. |
Make prohibited examples concrete
List the items people most often paste under time pressure: customer emails, account numbers, medical details, employee records, credentials and API keys, unreleased financial results, proprietary source code, legal advice, contracts, security test results, vendor data, procurement evaluations, and pre-decisional plans. Include images and attachments, not just typed text.
The Centers for Medicare & Medicaid Services (CMS) Information Security and Privacy Program states: “Never disclose or input PII, PHI, or any sensitive CMS data (including financial, health, vendor, procurement, evaluations, draft policies, or proprietary/business information) into publicly accessible AI platforms, chatbots, or prompts.” That wording is a useful model because it names both regulated and ordinary business information.
Define safe transformations
Allow public use for genuinely public material and for synthetic examples created for training or experimentation. De-identification is not automatically safe: names, dates, locations, account references, unique job titles, and unusual facts can permit re-identification when combined. The workflow owner should confirm that identifiers have been removed, the remaining detail is necessary, and the risk of reconstruction is controlled.
Redaction must be irreversible before submission. Replacing a name with “John Smith” is not de-identification if the surrounding facts identify the person. When in doubt, move the task to the approved enterprise environment or ask the privacy owner.
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Write a short acceptable-use policy
A policy should answer five questions in language employees can remember:
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- Which AI tools and accounts are approved?
- What information may be entered, uploaded, or generated?
- Are prompts, files, and outputs retained, and who can access them?
- How may an output be used, checked, and shared?
- Who can approve an exception and how is it recorded?
State that public AI services are external recipients. Prohibit confidential, regulated, and restricted data unless a specifically approved service and workflow cover that classification. Require employees to use secure official channels and share sensitive information only with authorized personnel who have a legitimate need to know.
Do not create a rule that says simply “never use AI.” Federal guidance favors risk-based controls where safe, low-risk uses can continue. Permit public information, approved synthetic data, brainstorming that contains no internal context, and other explicitly listed cases. Block high-risk categories and route exceptions to security and privacy owners.
Policy language employees can follow
Use only the AI tools listed in the approved-tools register. Do not enter, upload, or disclose PII, PHI, credentials, confidential or restricted business information, source code, contracts, security findings, vendor data, unreleased results, legal advice, or pre-decisional plans to a public AI service. Use public tools only for approved public information or confirmed synthetic examples. Use the managed enterprise AI workspace for internal work, follow its classification and access rules, verify every output, and report an accidental disclosure immediately through the privacy incident channel. Exceptions require written approval from the security and privacy owners before data is submitted.Publish the policy with a dated approved-tools register. Vendor retention, training use, processing locations, and features change; an approval that was valid for one plan, region, or date may not cover another.
Provide a sanctioned AI alternative
Employees are more likely to follow a restriction when the approved path is as convenient as the public one. Offer a managed enterprise workspace or private deployment for work that needs internal context. “Enterprise” in a product name is not proof of protection; verify the contract, tenant settings, and actual data flows.
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- Confirm whether prompts, files, and outputs are used to train or improve a shared model. Obtain a contractual prohibition where that is required.
- Document retention defaults, deletion controls, backup treatment, and the process for deleting a conversation or tenant.
- Require confidentiality obligations, limits on onward disclosure, incident-notification deadlines, and access for audits or investigations.
- Record processing locations and transfers. A geographic option is meaningful only when the contract and technical configuration enforce it.
- Understand how new models, plugins, connectors, and features are introduced and how customers are notified of material changes.
Identity and tenant controls
- Use single sign-on with multifactor authentication and disable shared accounts.
- Apply role-based access so a user can reach only the models, connectors, files, and assistants needed for the job.
- Keep each organization’s prompts, files, retrieval indexes, and administration separate from other tenants.
- Export audit logs to the security monitoring system, including user, time, tool, model, connector, action, and policy result.
- Define legal-hold, access-request, and deletion procedures before regulated data enters the service.
Integration with existing controls
The approved workspace should connect to your identity provider, data-classification labels, DLP system, records management, and incident-response process. If a connector can read a document repository, enforce the same permissions that apply in that repository; an AI assistant must not become a shortcut around file access rules.
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Enforce rules at the point of use
Policies educate, but technical controls stop a hurried or mistaken submission. NIST SP 1800-28 emphasizes identifying affected assets, assessing risk, and implementing protective technologies. The joint CISA guidance frames AI security as protecting, detecting, and responding across AI systems and the data connected to them.
Discover unsanctioned services
Build an inventory from browser, DNS, proxy, firewall, endpoint, identity, and API telemetry. Identify public chatbot domains, mobile applications, browser extensions, personal accounts, and developer APIs. Classify each destination as approved, conditionally approved, or blocked. Review the inventory regularly because new services and domains appear frequently.
Use an AI-aware DLP gateway
Place a gateway or DLP control between users and external AI services where technically and legally appropriate. It should inspect typed text, pasted content, uploads, images, and API requests for classification labels, personal identifiers, credentials, source-code patterns, and other sensitive signatures.
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- Warn: show the detected category and ask the user to remove or replace the data.
- Redact: mask identifiers before submission when the workflow has been approved for that transformation.
- Block: prevent transmission of regulated, restricted, or high-confidence secret data.
- Route: send the request to the managed enterprise workspace when an equivalent capability exists.
- Record: log the policy decision and minimal investigation context without retaining full prompt content unnecessarily.
Start in monitor or warn mode to tune false positives, then block high-confidence categories. Every exception should have an owner, purpose, expiry date, and documented compensating control.
Control browsers, endpoints, and APIs
Browser policies can block unsanctioned extensions, prevent personal-account sign-in on managed devices, and restrict file uploads. Endpoint controls can detect secrets copied to the clipboard or uploaded from protected folders. Network controls can block known destinations, while API management can require organization-owned keys and enforce rate, model, and data policies. No single layer sees every route, so combine them.
Protect credentials and source code
Scan prompts and attachments for private keys, passwords, tokens, connection strings, and proprietary code. If a secret is submitted, treat it as exposed: revoke or rotate it immediately, investigate access logs, and preserve the event for incident response. Do not assume that deleting the chat removes copies from provider logs or backups.
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Train employees for real decisions
Annual policy acknowledgment is not enough. CMS calls for continual education and reminders about the risks and consequences of sharing business data with public AI tools. Training should rehearse the decisions employees actually face, not just define “AI.”
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- Customer email: remove names and account details, create a synthetic example, or use the approved workspace.
- Contract review: do not upload the contract to a public chatbot; use an authorized legal workflow with the correct access controls.
- Code debugging: replace proprietary functions, endpoints, and keys with synthetic equivalents, or use the sanctioned coding assistant.
- Spreadsheet analysis: remove personal and financial fields and confirm that aggregation cannot identify individuals.
- Incident summary: keep evidence and indicators in the incident system, not in a public prompt.
Teach a five-second pause
- What classification does this text, file, image, or code carry?
- Would I send the same material to an external expert who is not under our contract?
- Is this destination on the approved-tools register, under the correct account and plan?
- Can I use public information or a synthetic example instead?
- What is the reporting channel if I make a mistake?
CMS offers a memorable test: if you hesitate to share information with an external expert, do not submit it to a generative AI platform. Put that test in onboarding, refresher training, browser warnings, and team checklists.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Detect, report, and contain accidental disclosures
Assume mistakes will occur and make the response fast and non-punitive. A clear reporting path is a preventive control because employees are more likely to disclose an error before a manager or monitoring system discovers it.
Monitor proportionately and lawfully
Monitor sanctioned and unsanctioned use where permitted by privacy law, employment rules, collective agreements, and works-council or employee-notice requirements. Tell employees what is monitored, why it is needed, who can see it, and how long records are retained. Prefer event metadata and policy outcomes over indiscriminate storage of complete prompts.
Useful alerts include prohibited-data matches, repeated blocked attempts, unusual upload volume, personal-account use on managed devices, and access to unapproved domains. Set thresholds with the security and privacy teams; an alert is a lead for review, not proof of misconduct.
Give employees an immediate response path
- Stop further sharing and do not try to “clean up” evidence by repeatedly editing or deleting conversations.
- Use the privacy or security incident channel immediately, even if the disclosure seems minor.
- State what was sent, when, to which service and account, whether a file or credential was included, and who may be affected.
- Ask the security team to revoke or rotate exposed credentials, disable sessions, and preserve relevant logs.
- Let privacy, legal, and records owners assess notification, contractual, regulatory, and retention obligations.
- Document the root cause and update the policy, DLP rule, training example, or approved workflow that would have prevented it.
CMS requires reporting suspected or verified privacy breaches and unintended disclosures. Your procedure should make that requirement operational with one channel, a defined triage owner, and an escalation route outside the employee’s direct management chain.
Compare approved AI options on risk, not labels
When selecting or renewing an enterprise AI service, use a written comparison. Record the answer for the exact plan, region, model, and contract date; defaults can differ by tier and can change after a feature update.
| Control area | Questions to document | Evidence to request |
|---|---|---|
| Training and retention | Are prompts or files used for provider training? What are the default and configurable retention periods? | Contract terms, product settings, deletion and backup procedures |
| Confidentiality | Are personnel and subprocessors bound to confidentiality? Is onward disclosure limited? | Data-processing agreement and subprocessor list |
| Identity and roles | Does it support SSO, multifactor authentication, least-privilege roles, and lifecycle automation? | Configuration documentation and test evidence |
| Tenant isolation | How are prompts, files, indexes, and administrative data separated? | Architecture description and independent assurance reports |
| Auditability | Which events are logged, how long are logs retained, and can they be exported to your SIEM? | Log schema, API documentation, sample export |
| Data geography | Where are prompts processed and stored, including backups and support access? | Region controls and contractual commitments |
| DLP integration | Can classification labels, redaction, blocking, and exception workflows be enforced before submission? | Connector and policy documentation |
| Change management | How are model, plugin, connector, and training-policy changes announced? | Notice terms and change-log process |
| Incident response | When will the provider notify you, and can you investigate or preserve evidence? | Incident clauses, contacts, and runbooks |
| Legal and records needs | Can the service support legal holds, access requests, deletion requests, and export? | Administrative procedures and API capabilities |
The UK Information Commissioner’s Office has emphasized that security measures should match the specific processing risk rather than follow a one-size-fits-all rule. Its guidance page is marked as under review following UK legislative changes, so obtain current jurisdiction-specific advice before relying on it for a legal conclusion.
A practical rollout plan
First 30 days: establish control
- Name accountable owners from security, privacy, legal, IT, records, and the business units using AI.
- Publish an interim prohibition on regulated, confidential, and restricted data in public tools.
- Create the approved-tools register and identify high-risk public destinations.
- Enable basic domain and account controls on managed browsers and endpoints.
- Open a single reporting channel and brief managers on immediate containment steps.
Days 31–60: make the safe path usable
- Choose or configure a managed enterprise workspace after reviewing retention, training use, confidentiality, identity, geography, logs, DLP, and incident terms.
- Connect SSO, multifactor authentication, role groups, classification labels, and audit export.
- Deploy DLP detection in monitor or warn mode and tune rules with representative synthetic data.
- Train employees with customer-email, contract, code, spreadsheet, and incident scenarios.
- Document the exception process, including approvers, expiry dates, and compensating controls.
Days 61–90: block, measure, improve
- Block high-confidence secrets, regulated identifiers, and restricted classifications at the gateway.
- Route eligible internal requests to the approved workspace and test connector permissions.
- Run a controlled exercise in which a test user reports an accidental disclosure.
- Review blocked events, false positives, repeat attempts, and unapproved-tool discovery without retaining unnecessary prompt content.
- Update policy language, training, detections, and vendor assessments from the findings.
Common failure modes and fixes
- Blanket ban with no alternative: employees move to personal accounts. Provide a managed tool for legitimate work and explain permitted low-risk uses.
- Policy without enforcement: rules are forgotten under deadline pressure. Add browser, endpoint, identity, API, and DLP controls.
- Blocking only one website: users switch to another service or a mobile app. Maintain an inventory and monitor destinations, extensions, and APIs.
- “Anonymized” data that remains identifiable: combinations of fields reveal a person. Use synthetic data or obtain privacy-owner confirmation.
- Trusting an enterprise label: defaults may still allow retention or training use. Verify the exact contract, plan, region, and settings.
- Logging everything forever: monitoring creates a second sensitive repository. Minimize prompt content, set retention limits, restrict access, and disclose monitoring practices.
- Punishing the first report: employees hide mistakes. Use a no-blame first-response process while preserving accountability for deliberate violations.
- Ignoring outputs: a safe input can still produce confidential material or an inaccurate decision. Apply access controls, human review, and output-handling rules.
Governance that keeps the program current
Review the approved-tools register and controls on a defined schedule and whenever a provider changes retention, training use, processing geography, connectors, or model behavior. Revalidate high-risk workflows after organizational changes, new regulations, or an incident.
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Use measurable signals without inventing a false safety score: percentage of employees trained and refreshed, coverage of managed devices and accounts, number and category of blocked submissions, repeat attempts, time from disclosure to report, time to credential rotation, exception age, and completion of provider reviews. Interpret trends with context; a rise in reports can indicate better trust rather than worse behavior.
Keep a decision record for each approved use case: data classification, business purpose, model and connector, human reviewer, retention setting, geographic scope, DLP rule, owner, and review date. That record lets security and privacy teams demonstrate why a workflow is allowed and close it when its risk changes.
Bottom line
Preventing confidential data leakage to public AI is an operational system: clear classification, a concise rule, a genuinely useful approved alternative, controls that inspect and block at submission, continuous scenario-based training, and rapid reporting and containment. Implement those layers together, verify provider terms for the exact service and date, and leave low-risk experimentation available only where the organization can explain and enforce the boundaries.
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