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Do not let employees paste company information into public AI tools by default. Set a risk-based policy that classifies data before prompting, keeps an approved-tool register, requires vendor due diligence and technical safeguards, trains people by role, and makes a human accountable for every consequential output. Public or low-risk information can be allowed in approved tools; confidential, personal, regulated, security-sensitive, credential, and trade-secret data should be prohibited unless the organization has documented security, privacy, legal, and business-owner approval.
The policy should cover employees, contractors, interns, and vendors, and it should apply to chatbots, copilots, APIs, transcription, image and code generators, and AI features embedded in ordinary software. The sections below provide a practical structure, approval workflow, training plan, and incident process you can adapt to your organization.
What should an employee AI-use policy accomplish?
A useful policy enables legitimate productivity while preventing uncontrolled disclosure, unreliable decisions, and unreviewed automation. It should answer five questions for every proposed use: what data is involved, which tool will process it, what the tool and vendor do with that data, how consequential the output is, and who remains accountable.
Use the policy as an operating control, not a one-time memo. Assign owners, record decisions, monitor incidents, and review the rules when models, contracts, laws, or business processes change.
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Define the people and systems in scope
Include permanent and temporary employees, contractors, interns, agency staff, and vendors acting for the organization. Scope every AI capability that can receive, infer, generate, transform, summarize, classify, transcribe, or recommend information, including public chatbots, enterprise copilots, application programming interfaces, meeting transcription, image and video tools, code assistants, workflow automations, and embedded features in office, customer-service, recruiting, or analytics products.
State the accountability rule
An AI system may assist with work, but a named employee and the responsible business owner remain accountable for the decision, communication, or record produced. No user may transfer professional, legal, clinical, financial, employment, safety, or security responsibility to a model.
How do you classify company data before an AI prompt?
Require users to classify the input and the expected output before opening an AI tool. When a prompt combines categories, apply the strictest category. Treat derived information, summaries, embeddings, transcripts, screenshots, and code snippets as containing the sensitivity of the source unless an approved review establishes otherwise.
| Data category | Examples | Default AI handling | Approval and safeguards |
|---|---|---|---|
| Public | Published web copy, public product documentation, released financial results | May be used in an approved tool for ordinary business work | Use an approved tool, follow copyright rules, and review the output |
| Internal or low-risk | Non-public process notes or material whose disclosure would cause limited harm | Allowed only in tools listed for that category | Access controls, appropriate retention terms, and human review |
| Confidential | Unreleased plans, customer proposals, pricing, internal reports, source code, or contracts | Prohibited in public or otherwise unapproved tools | Written approval from security, privacy or legal as applicable, and the data owner; use contractual and technical controls |
| Personal | Names linked to records, contact details, identifiers, employment information, or any information about an identifiable person | Do not enter into a tool unless its processing is specifically approved | Document the lawful basis and purpose, minimize fields, control access, set retention and deletion terms, and assess cross-border processing |
| Regulated or highly sensitive | Health, financial, children’s, protected-class, government-identity, or other legally protected information | Prohibited by default | Require privacy, legal, security, and business-owner approval plus controls appropriate to the sector and jurisdiction |
| Security-sensitive | Passwords, API keys, private certificates, vulnerability details, security configurations, incident evidence, or bypass methods | Never place in public AI tools; do not disclose credentials to any model | Use a specifically approved environment with least privilege, isolation, logging, and secure deletion; report accidental disclosure immediately |
| Trade secret or strategic | Proprietary algorithms, research, designs, acquisition plans, or information protected by confidentiality obligations | Prohibited unless an explicitly approved workflow exists | Confirm ownership and contractual rights, restrict access, and document the business owner’s approval |
When classification is uncertain, stop and ask the data owner, privacy contact, or security team. Convenience is not a reason to downgrade a classification.
Can employees paste company data into ChatGPT or another public chatbot?
Only when the specific tool and data category are explicitly approved. A consumer or public chatbot should be treated as an external processor with unknown or unsuitable retention, reuse, hosting, access, and deletion terms until due diligence and a contract establish otherwise.
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The Internal Revenue Service’s AI Governance Policy warns that entering any information, including seemingly innocuous work-related matters, into public or non-Treasury AI tools can pose significant risks. Make that principle operational: block unapproved tools where feasible, teach employees to recognize indirect disclosure, and provide an approved alternative for legitimate work.
Examples of information that must not be pasted into an unapproved tool
- Passwords, access tokens, private keys, recovery codes, or security-control bypass instructions.
- Customer, patient, employee, applicant, student, or partner records that identify a person.
- Unreleased financial results, merger or acquisition information, product road maps, or confidential negotiations.
- Source code, proprietary models, trade secrets, vulnerability reports, and incident artifacts.
- Contract text or third-party information whose terms prohibit onward disclosure.
Safer ways to ask for help
- Replace real values with clearly synthetic examples and verify that the transformation cannot be reversed.
- Ask for a general method, checklist, or explanation rather than submitting the underlying record.
- Use a tool approved for the classification, with the minimum fields needed for the task.
- Remove hidden metadata, comments, tracked changes, screenshots, and attachments that may contain sensitive material.
How do you build an approved AI-tool register?
Maintain a central inventory rather than relying on informal recommendations. Every entry should state what the tool is approved to do, what data it may receive, who owns it, and when the approval must be reviewed.
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| Register field | What to record |
|---|---|
| Tool and version | Product name, edition, enabled AI features, model or service version when available, and the date checked |
| Business owner | The person accountable for the use case, budget, outcomes, and renewal |
| Technical owner | Administrator responsible for configuration, identity integration, logs, and access removal |
| Permitted use case | Specific tasks, user groups, jurisdictions, and outputs allowed |
| Permitted data | Classification levels, fields, file types, and prohibited content |
| Retention and reuse | How prompts and outputs are stored, whether they train or improve a service, retention period, deletion process, and backup treatment |
| Hosting and transfers | Processing locations, cross-border transfers, and applicable regional restrictions |
| Security controls | Single sign-on, multifactor authentication, role-based access, encryption, tenant isolation, audit logs, rate limits, and administrator controls |
| Subprocessors | Relevant subcontractors, notification terms, and the organization’s objection or termination rights |
| Contract terms | Confidentiality, data-use limits, breach notification, deletion on exit, audit or assurance rights, indemnity where negotiated, and service-level commitments |
| Risk and approval record | Impact assessment, security and privacy findings, legal review, business-owner decision, conditions, approvers, and expiry or review date |
What vendor due diligence should test
Ask how the provider tests models, manages human and automated oversight, handles privacy and security, restricts administrator and support access, responds to incidents, and permits deletion. The Australian Office of the Information Commissioner advises organizations to examine testing, oversight, privacy, security, and access when assessing AI that processes personal information. Record unanswered questions as conditions or reasons to reject the tool; do not treat marketing language as evidence.
Set approval tiers
- Standard approval: public or low-risk data, low-impact tasks, approved contract, and routine human review.
- Enhanced approval: confidential data, external-facing content, automated actions, large-scale processing, or material operational impact. Require security, privacy or legal review as relevant.
- Restricted approval: regulated data, high-impact decisions, safety or security operations, or cross-border processing with significant legal implications. Require the business owner and designated control functions to sign off, with a documented test and monitoring plan.
What uses should an employee AI policy prohibit?
Write prohibitions in plain language and pair each one with a safe alternative. The following activities should be banned unless a formally approved exception is documented:
- Sharing credentials, secrets, private keys, or authentication codes with an AI system.
- Uploading personal, confidential, regulated, security-sensitive, or proprietary data to an unapproved service.
- Attempting to bypass security controls, safeguards, access restrictions, licensing, or usage limits with generated instructions or code.
- Generating deceptive, discriminatory, harassing, malicious, or unlawful content.
- Making employment, credit, insurance, healthcare, legal, safety, disciplinary, or other consequential decisions solely from AI output.
- Presenting generated text, code, images, or analysis as verified fact or original work when it has not been checked and attribution or licensing rules require disclosure.
- Connecting an AI tool to production systems, customer communications, payment systems, or repositories without an approved integration and rollback plan.
Require a higher approval threshold for high-impact or regulated workflows. A use that is acceptable for brainstorming may be unacceptable for a customer decision, an employment action, or an automated change to a live system.
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How should human review work?
Human review must be specific enough to detect predictable failures, not a checkbox saying that someone glanced at the result. The reviewer should understand the task, have access to trustworthy sources, and be able to reject or correct the output.
Use this review checklist
- Accuracy: Check calculations, quotations, citations, code behavior, and factual claims against authoritative sources.
- Currency: Confirm that laws, prices, product details, policies, and operational data are current for the relevant date and jurisdiction.
- Bias and fairness: Look for stereotypes, proxy discrimination, unequal treatment, and missing perspectives.
- Confidentiality: Ensure the output does not reveal source data, personal information, hidden instructions, or another party’s confidential material.
- Copyright and permissions: Verify that inputs and outputs can legally be used, shared, modified, and attributed in the intended context.
- Suitability: Confirm that the tone, reading level, accessibility, security posture, and business decision fit the audience.
- Traceability: Keep the prompt, relevant source material, model or tool version, reviewer, and final changes when the use case requires an auditable record.
CMS responsible-use guidance, last reviewed on 26 August 2025, says users should not rely solely on AI output and should validate it with trustworthy sources and independent research. Apply that standard especially to anything sent externally or used to make a decision about a person.
How do you train employees to use AI safely?
Deliver short, role-specific instruction before access is granted, then refresh it when tools or risks change. The European Union AI Act’s Article 4 requires providers and deployers to take measures supporting staff AI literacy; the requirement began applying on 2 February 2025. European Commission FAQs explain that there is no one-size-fits-all training format and that organizations may keep internal training records.
Core lessons for everyone
- Which tools are approved, how to sign in, and where to report a problem.
- How to classify data and recognize indirect disclosures in prompts, files, images, transcripts, and code.
- Why models can hallucinate, omit context, reproduce bias, or expose sensitive details.
- How to write prompts that minimize data, use synthetic examples, and state the required output and constraints.
- How to review accuracy, currency, confidentiality, copyright, and fairness before use.
- What the policy prohibits and how accountability remains with the employee and business owner.
- How to preserve evidence and report a suspected disclosure or harmful output.
Additional training by role
- Managers and approvers: risk classification, proportionate approvals, monitoring, and documenting exceptions.
- Developers and analysts: secret management, code review, dependency and license checks, testing, and production separation.
- Human-resources, legal, healthcare, finance, and other regulated teams: high-impact decision rules, records management, privacy obligations, and escalation.
- Administrators: identity, access groups, retention settings, logging, tenant configuration, and disabling accounts promptly.
- Incident responders: containment, evidence preservation, notification decisions, and coordination with privacy, security, legal, and communications teams.
Measure completion and practical competence, not attendance alone. Use scenario exercises such as a pasted customer spreadsheet, a generated hiring recommendation, or a code assistant requesting a secret. Keep the learner’s role, course version, completion date, assessment result, and next review date.
What records and roles are needed for accountability?
Name a policy owner who can update the rules and convene reviews. For each approved tool and use case, identify a business owner, technical owner, data owner, privacy contact, security contact, legal reviewer where needed, approver, and incident route.
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Maintain these records
- An inventory of AI tools, enabled features, owners, data permissions, contracts, and review dates.
- An AI-use-case register describing purpose, users, data categories, impact, jurisdiction, approval conditions, and monitoring measures.
- Approval and exception decisions, including the rationale, compensating controls, approvers, expiry date, and renewal decision.
- Training records and evidence that staff understood role-specific requirements.
- Incident, near-miss, complaint, correction, and model-performance records, subject to lawful retention limits.
The NIST Generative AI Profile, NIST AI 600-1 (2024), recommends documented roles, communication lines, incident procedures, and trained incident responders. NIST AI RMF 1.0, published in 2023, is intended for voluntary use, but its governance concepts provide a practical way to organize these records.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should employees do after an accidental disclosure?
Make reporting immediate and non-punitive for good-faith mistakes. Delayed reporting can allow a provider, recipient, or connected system to retain or act on the information.
- Stop the activity: do not send additional prompts, delete or revoke the exposed credential if possible, and disconnect an unapproved integration.
- Report through the defined route: contact the security or privacy incident channel and the manager or tool owner without waiting to determine the full impact.
- Preserve evidence lawfully: record the tool, account, time, prompt, files, recipients, settings, model version, and visible response; do not copy sensitive material into a new system.
- Contain and assess: security and privacy teams should determine access, retention, onward disclosure, affected people, contractual duties, and whether legal or regulatory notification is required.
- Remediate: rotate secrets, remove access, request deletion where contractually available, correct or retract harmful output, and notify affected users when the responsible team decides it is necessary.
- Learn: document the cause, update controls or training, and record the lesson in the next policy review.
CMS guidance requires reporting suspected or verified privacy breaches or unintended disclosures. Your policy should define the reporting channel, response targets, evidence rules, and notification authority before an incident occurs.
How often should an AI policy and tool approval be reviewed?
Review the policy and every approved tool at least annually, and sooner after a material change. A material change includes a new model or feature, altered retention or training terms, a new subprocessor or hosting region, a security incident, a change in data classification, a new jurisdictional requirement, or a proposed high-impact use.
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- Are the data classifications and prohibited examples still accurate?
- Do current contracts still prohibit unapproved reuse and provide workable deletion and breach terms?
- Are access groups, logs, retention settings, and integrations configured as approved?
- Have incidents, near misses, complaints, or model-behavior changes altered the risk?
- Do training materials match the tools and workflows employees actually use?
- Should an approval be renewed, narrowed, suspended, or withdrawn?
Give each approval an owner and a review date. Expired approvals should automatically move to a restricted state until renewed, rather than continuing indefinitely by default.
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How can you roll out the policy without blocking useful work?
A staged rollout reduces shadow use and gives teams a safe path for common needs.
- Map current use: survey teams, browser and network logs where lawful, procurement records, and support tickets to identify tools and workflows already in use.
- Set the minimum rules: publish the data-classification matrix, prohibited uses, reporting route, and human-review requirement before negotiating every tool.
- Approve a small starter set: choose tools whose contracts, access controls, retention, and administrative settings meet the organization’s risk level.
- Provide sanctioned patterns: offer synthetic-data examples, prompt templates, review checklists, and approved integrations so employees have a practical alternative to copying data into public services.
- Train and measure: require role-based instruction, test understanding, and monitor adoption, exceptions, incidents, and blocked attempts.
- Expand deliberately: add tools or higher-risk uses only after documented review, an accountable owner, and a monitoring and rollback plan exist.
Do not promise that an approved tool is risk-free. Approval means that its identified use, data, controls, and oversight are acceptable under stated conditions; changing any of those conditions requires a new decision.
Frequently Asked Questions
Can employees use an AI tool for public marketing copy without approval?
Public text may be low risk, but the tool still needs to be on the approved register if employees use organizational accounts or publish the result. Review copyright, factual accuracy, confidential context hidden in prompts or attachments, and any customer-facing claims before publication.
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Make compliance a contractual and onboarding requirement, and limit access to approved tools and data for the assigned work. Require the sponsoring business owner to confirm training, access removal at termination, incident reporting, and any subcontractor restrictions.
What should be logged for an AI-assisted decision?
Record the use case, tool and version, date, data category, relevant prompt or input description, source material, reviewer, changes made, final decision, and approval or exception. Keep only what is necessary under privacy and records-retention rules, and protect the log as sensitive business information.
Should an organization block every public AI website?
Blocking can reduce accidental disclosure, but it does not replace a usable approved service, training, or monitoring. Apply technical restrictions proportionately, provide sanctioned alternatives, and make the policy clear about personal accounts and unapproved browser extensions.
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