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Do not let employees paste sensitive company data into public or unapproved AI assistants. A secure policy uses a default-deny rule, classifies data before prompting, approves specific tools and use cases, limits vendor handling, requires technical safeguards and human review, and defines training, records, incident response, and periodic review. Employees may use an assistant with confidential information only when the company has approved that exact environment, purpose, data class, users, and controls.
The policy should be a living control rather than a one-time memo. Review it at least annually and whenever a model, vendor, connector, contract, law, or business use changes. The framework below turns that principle into rules, approval records, technical requirements, and procedures that a company can implement.
What should a secure AI-use policy accomplish?
The policy must make AI use governable: it should state who may use which assistants, for what work, with which data, under what contractual and technical controls, and with what review and reporting duties. It should apply to employees, contractors, temporary staff, and anyone else who can access company information.
Write the policy around these outcomes:
- Confidentiality: prevent prompts, files, outputs, and connected data from being disclosed to unauthorized people or services.
- Integrity: detect inaccurate, manipulated, biased, or malicious output before it influences work.
- Availability: keep critical workflows functioning if an AI service is unavailable, compromised, or withdrawn.
- Accountability: identify the user, approved purpose, data involved, reviewer, and decision made.
- Privacy and legal compliance: minimize personal data, preserve privilege, respect contractual duties, and meet applicable notification and retention requirements.
State that the policy overrides convenience. A user must not bypass an approval or paste restricted material simply because an unapproved assistant produces a better answer or has a free tier.
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How should the policy classify data before anyone writes a prompt?
Require a data classification decision before every prompt, upload, connector, or automated action. If a user cannot confidently classify the material, the material must be treated as the higher-risk category until the data owner or privacy team decides otherwise.
Use a practical classification scheme
| Class | Examples | Default AI rule |
|---|---|---|
| Public | Published web copy, public filings, released product documentation | Permitted in approved or public tools only when no confidential context is added and copyright or license terms allow the use. |
| Internal | Routine procedures, non-public schedules, internal announcements | Use only an organization-approved assistant with access controls and logging. |
| Confidential | Customer lists, pricing plans, contracts, source code, security details, vendor terms | Require an approved environment, documented purpose, least-privilege access, and human review. |
| Personal information | Names linked to identifiers, contact details, employee records | Minimize and redact; use only where the approved purpose and legal basis permit it. |
| Sensitive personal information | Health, biometric, precise location, financial, government-identification, or similar data | Prohibit in public tools; require a specifically approved workflow, stronger controls, and a documented legal basis. |
| Regulated, privileged, or trade-secret data | Protected health information, legal advice, export-controlled material, investigations, unreleased inventions | Default deny. Permit only after security, privacy, legal, and business-owner approval for that exact use. |
| Pre-decisional or restricted information | Draft policies, acquisition plans, disciplinary matters, unreleased decisions | Keep out of public and unapproved assistants; use a controlled tenant only when the owner authorizes it. |
Make minimization mandatory. Remove names, account numbers, exact dates, identifiers, unnecessary free text, and attachments that are not needed for the task. Prefer a synthetic example or a de-identified excerpt. Redaction is not a license to include the rest of a sensitive record: combinations of fields can re-identify a person or reveal a trade secret.
The Australian Office of the Information Commissioner (OAIC, 2024) cautions that public availability does not automatically make data lawful for AI training or use. Sensitive information generally needs consent or another valid legal basis, and unnecessary information should be excluded or deleted. Put that principle in the policy rather than assuming employees know it.
Can employees paste confidential information into ChatGPT or another public assistant?
No, not unless the company has approved that service, configuration, purpose, and data class. The default rule should prohibit public or unapproved tools from receiving personally identifiable information (PII), protected health information (PHI), financial data, customer or employee records, vendor and procurement details, security information, legal-privileged material, export-controlled information, trade secrets, or pre-decisional documents.
Use a prominent rule such as: “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.” This is the Centers for Medicare & Medicaid Services (CMS) wording from its Guidance for Responsible Use of Artificial Intelligence, last reviewed 2025; adapt the examples to your own classifications.
Allow public tools only for genuinely public material, such as brainstorming from a released press statement, and require the user to remove confidential context. A public prompt can still create risk when the user combines harmless-looking facts with internal strategy, a customer identity, or a unique incident description.
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How do we approve an AI assistant and a specific use case?
Create an approval gate before use with anything above public data. A tool is not approved merely because it is popular, has a business subscription, or advertises enterprise security. Approval applies to the combination of provider, model, tenant, region, connectors, users, data classes, and purpose.
Record these fields in an AI-use register
- Assistant, model version, provider, owning department, and business owner.
- Approved purpose and prohibited purposes.
- Permitted data classes and maximum sensitivity.
- Authorized user groups, administrator roles, and contractor access.
- Processing region, subprocessors, cross-border transfers, and hosting arrangement.
- Prompt and file retention period, training use, human access, and deletion method.
- SSO, multifactor authentication, role controls, tenant isolation, encryption, DLP, logging, and connector restrictions.
- Incident contacts, breach-notification terms, support for access or deletion requests, and audit evidence.
- Approval date, expiry or review date, contract reference, and conditions that would suspend approval.
Require sign-off from security, privacy, legal, procurement, and the business owner. For regulated or high-impact uses, add the relevant compliance, safety, records, or works-council stakeholders. Record the decision even when the answer is “not approved”; a searchable register prevents repeated requests and exposes shadow use.
Use a staged approval path
- Describe the task. State what the assistant will do, who benefits, what decisions it may influence, and what happens if it is wrong.
- Map the data flow. List every prompt field, uploaded file, connector, output destination, human reviewer, and downstream system.
- Classify the data. Apply the highest classification present, including data inferred by combining fields.
- Assess the provider. Confirm retention, model-training use, human access, subprocessors, regions, deletion, breach notice, and contract terms.
- Test controls. Verify identity, permissions, tenant separation, logging, DLP or redaction, connector scopes, and export or deletion functions.
- Define review. Name a qualified reviewer and specify accuracy, bias, privacy, security, copyright, and suitability checks.
- Set an expiry. Approvals should have an owner and review date, and should automatically lapse when a material model, contract, connector, or purpose changes.
What vendor and contract controls are essential?
Do not rely on a provider’s marketing page or a default setting. The contract, order form, data-processing terms, and administrator console should agree on how prompts, files, outputs, telemetry, and account data are handled.
- State whether prompts and uploads are retained, for how long, and in which region.
- Prohibit using company content to train a shared model unless the company has expressly approved that use.
- Identify every subprocessor and require notice and an objection or termination path for material changes.
- Limit human access to support personnel with a documented need, and require confidentiality controls.
- Define encryption in transit and at rest, key-management responsibilities, and tenant isolation.
- Provide deletion, correction, export, legal-hold, and data-subject-access support that matches the company’s obligations.
- Set a clear security-incident notice deadline, cooperation duties, evidence preservation, and remediation process.
- Permit independent assurance or audit evidence, such as relevant control reports and penetration-test summaries.
- Allow the company to disable users, revoke tokens, remove connectors, and terminate access promptly.
The Internal Revenue Service (IRS, 2026) warns that publicly available platforms typically “retain, reuse, or resell entered information,” potentially exposing internal strategies, plans, or intentions. Your policy should tell users that an input can remain a provider-side business record even when the chat window looks temporary.
Which technical safeguards should an approved assistant use?
Separate low-risk experimentation from production or sensitive workflows. The least permissive architecture that can perform the approved task is usually the safest choice.
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- Use company SSO and multifactor authentication; prohibit shared accounts.
- Assign role-based permissions for users, administrators, reviewers, connectors, and exports.
- Apply least privilege to connected drives, mailboxes, ticket systems, code repositories, and databases.
- Review membership and administrator rights regularly, and revoke access when a person changes role or leaves.
Data protection
- Use an isolated organizational tenant rather than a personal account.
- Encrypt data in transit and at rest, and document who controls the keys where applicable.
- Deploy DLP, pattern detection, tokenization, or redaction before data reaches the model.
- Restrict file uploads, clipboard transfer, downloads, copy-and-paste, and external sharing according to risk.
- Set retention limits and securely delete temporary files, embeddings, cached results, and conversation exports.
Connectors and autonomous actions
- Disable plugins, browsing, code execution, and connectors by default.
- Approve each connector separately; specify allowed repositories, fields, actions, and rate limits.
- Require confirmation before sending messages, changing records, publishing content, executing code, or triggering payments.
- Block prompt-injection paths in untrusted documents and treat retrieved text as data, not instructions.
Monitoring and resilience
- Log user, time, tool, data classification, connector, action, reviewer, and policy decision without collecting unnecessary personal content.
- Alert on unusual volume, repeated blocked prompts, mass downloads, privilege changes, and attempts to bypass controls.
- Maintain rate limits, abuse detection, backup procedures, and a non-AI fallback for critical operations.
Joint guidance from the Cybersecurity and Infrastructure Security Agency and international partners (2024) describes secure deployment and operation of externally developed AI systems. Use its confidentiality, integrity, availability, protection, detection, and response goals as the security baseline for your architecture.
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How should employees review AI output before using it?
Require a qualified human to review every output that will be published, sent externally, stored as a business record, or used in a decision. The reviewer remains accountable; “the model said so” is not an approval.
Make the reviewer check five risks
- Accuracy: verify factual claims, calculations, quotations, dates, and references against trustworthy sources.
- Bias and fairness: look for discriminatory assumptions, unequal recommendations, and missing perspectives.
- Privacy: remove personal data that is not necessary for the audience or purpose.
- Security: inspect links, code, attachments, and instructions for malware, prompt injection, secrets, or unsafe actions.
- Suitability and rights: confirm that the result meets the task, confidentiality, copyright, accessibility, and records requirements.
Do not make employment, credit, health, safety, legal, or other consequential decisions solely from unverified AI output. CMS guidance says users should validate outputs with trustworthy sources, expert consultation, and independent research. Record the reviewer, sources checked, material edits, and final version for decisions that require an audit trail.
What records should the company retain?
Keep evidence proportionate to the risk and lawful retention requirements. At minimum, maintain the AI-use register, approval and risk assessments, contract and configuration evidence, training completion, material prompts or input descriptions where lawful, outputs used in decisions, reviewer identity, source checks, incidents, and corrective actions.
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The Information Commissioner’s Office (ICO, accessed 2026) recommends documenting data movement and storage, maintaining clear audit trails, deleting intermediate personal-data files when no longer required, and considering de-identification or privacy-enhancing technologies. Build those actions into the workflow instead of leaving them to individual discretion.
How should the company train employees and contractors?
Training is mandatory before access and refreshed when the policy or tools change. It should be role-specific: a casual user needs prompt-minimization skills, while an administrator also needs connector, logging, incident, and access-review training.
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- Data classes, minimization, redaction, synthetic examples, and prohibited inputs.
- Approved assistants, approved purposes, account security, SSO, MFA, and reporting channels.
- Hallucinations, bias, uncertainty, source verification, and safe handling of generated code.
- Privacy, intellectual property, confidentiality, records, export controls, and contractual duties.
- Prompt injection, malicious files, unsafe links, data exfiltration, and autonomous-action risks.
- How to document a material use, preserve evidence, and obtain human approval.
Use practical exercises: show a realistic customer email, ask learners to classify and redact it, then have them identify a safe alternative prompt. Contractors and temporary staff need the same restrictions and an explicit offboarding process. Guidance from the Australian Signals Directorate’s Australian Cyber Security Centre (2024) addresses organizations using both self-hosted and third-party AI systems; CMS also calls for user understanding, stakeholder communication, documentation, and continuing education.
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What should an AI-data incident playbook contain?
Require immediate reporting of accidental disclosure, suspicious output, prompt injection, malicious files or connectors, account compromise, unexpected retention, or any attempt to bypass the policy. Do not punish good-faith reporting; delay increases exposure.
- Report and triage: capture who, what tool, when, what data, which recipients, and whether an action occurred.
- Contain: revoke sessions and API tokens, disable the assistant or connector, remove shared links, and isolate affected accounts or systems.
- Preserve evidence: retain relevant logs, prompts, files, outputs, configuration, contract terms, and timestamps without spreading the data further.
- Assess impact: identify affected people, data classes, jurisdictions, model or provider access, and possible downstream use.
- Notify and coordinate: involve security, privacy, legal, communications, the data owner, and regulators or affected parties when required.
- Remediate: delete or quarantine exposed data where possible, rotate secrets, correct outputs, patch controls, and retrain users.
- Learn: document root cause, update the approval record and policy, and verify that corrective controls work.
CMS requires reporting suspected or verified privacy breaches. CISA’s secure-deployment framing treats detection and response as core outcomes, not optional add-ons.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should we compare two or more approved assistants?
Use a written comparison and choose the least permissive tool that can perform the approved task. A model’s answer quality is only one factor.
| Comparison area | Questions to answer |
|---|---|
| Retention and training | Are prompts, files, outputs, and telemetry retained? Are they used for training or product improvement? |
| Isolation and location | Is the tenant isolated? Where are data and backups processed? Which subprocessors are involved? |
| Identity and administration | Are SSO, MFA, role controls, lifecycle automation, and separate administrator duties available? |
| Protection | What encryption, DLP, redaction, secret scanning, upload limits, and connector permissions exist? |
| Auditability | Can logs be exported with user, action, time, connector, and policy details? |
| Deletion and holds | Can the company delete prompts and files, honor legal holds, and support access requests? |
| Incident response | What notice deadline, cooperation, evidence, and service-disable mechanisms are contractual? |
| Quality and limitations | What known failure modes, content restrictions, language limits, and update practices affect the use case? |
| Cost and operations | What are licensing, storage, connector, implementation, monitoring, and exit costs? |
Mark any item that is not established as “not stated” and treat the uncertainty as a risk requiring clarification or a narrower approval.
How do we publish and enforce the policy?
Publish one concise user standard backed by technical controls, a register, and an escalation path. A policy that only says “be careful” will not stop shadow use.
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- Appoint an owner in security or privacy and name data owners for each restricted class.
- Inventory existing assistants, browser extensions, plugins, API keys, personal accounts, and automated workflows.
- Block or isolate unapproved services at identity, network, endpoint, proxy, and DLP layers where feasible.
- Publish the approved-tool catalogue with permitted data classes and purposes, not just product names.
- Require an approval record and training completion before granting access.
- Measure blocked prompts, exceptions, incidents, access reviews, training, deletion requests, and overdue approvals.
- Apply proportionate consequences for bypassing controls, while preserving a rapid route to report mistakes.
Review risk appetite, tools, data classes, contracts, logs, incidents, and legal requirements at least annually and whenever a material model, vendor, connector, or use case changes. The ICO notes that AI adoption can exacerbate existing risks and requires organizations to reconsider governance and risk-management practices.
What should the policy say about exceptions?
Allow exceptions only in writing, for a defined purpose, data class, user group, tool configuration, and time period. The request should explain why an approved alternative cannot perform the task, what additional safeguards will apply, who accepts the risk, and when the exception expires.
Never create a blanket exception for a department or “urgent” work. Emergency use should still require notification, minimum necessary data, a named approver, and a retrospective review. An exception that becomes recurring use belongs in the normal approval process.
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Use a short rule at the top of the standard: Classify first. Use only an approved assistant for the approved purpose. Share the minimum necessary data. Verify every consequential output. Record and report. Follow it with the detailed classifications, catalogue, approval form, technical baseline, review checklist, and incident contacts. The short rule drives behavior; the supporting controls make it enforceable.
Frequently Asked Questions
Can I use a free AI account for a harmless internal draft?
Only when the content is genuinely public and the draft contains no confidential context, personal data, secrets, or restricted facts. If the draft supports company work, use the approved organizational environment so access, retention, and deletion are governed.
Does turning off model training make a public assistant safe for sensitive data?
No. Training settings address only one handling question. Retention, human support access, subprocessors, regional transfers, logs, connectors, account compromise, and contractual deletion still require approval.
What should I do if I already pasted sensitive information into an unapproved tool?
Report it immediately through the security or privacy channel with the tool, time, data types, account, and any recipients. Do not delete or alter evidence first; responders may need logs and provider records to contain access and determine notification duties.
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Use a single owner with documented security, privacy, legal, and business-owner checks, a short inventory, and a narrow approved catalogue. Start with public-data use, then expand only when vendor terms, access controls, logging, deletion, and review responsibilities are documented.
When should an approved AI use be re-approved?
Re-open approval when the model, provider, tenant, processing region, connector, contract, data class, user population, or business purpose changes. Also review after an incident, a material control failure, or a new legal requirement rather than waiting for the annual cycle.
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