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Set a risk-based policy before employees use generative AI with work information. Require data classification before every prompt, limit use to an approved-tool register, prohibit confidential and regulated information in unapproved public services, and make a trained person accountable for checking every consequential output. The policy should cover employees and third parties, define vendor and security checks, require incident reporting, and be reviewed at least annually and after material changes.
This approach allows useful experimentation with public or low-risk data while creating clear approval gates for personal, proprietary, security-sensitive, and regulated information.
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A workable policy connects four decisions: what data is involved, which tool will process it, what the output will influence, and who remains responsible. It should be short enough to use during a workday but backed by detailed procedures for approvals, training, monitoring, and incidents.
At minimum, require employees to:
- Classify information before entering it into a prompt, upload, transcription, code assistant, image generator, API, or embedded AI feature.
- Use only tools and configurations listed in the organization’s approved-tool register.
- Follow the data, retention, access, and geographic restrictions recorded for that tool and use case.
- Check output for accuracy, bias, currency, confidentiality, copyright, and suitability before sharing or acting on it.
- Keep a human decision-maker accountable, especially for employment, health, financial, legal, safety, or other high-impact decisions.
- Report suspected disclosure, privacy, security, or policy violations immediately through a named channel.
The policy owner should publish a plain-language standard and link it to more specific procedures for procurement, information security, privacy, records management, and incident response.
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Who and what does the policy cover?
Apply the rules to employees, contractors, interns, temporary workers, consultants, and vendors acting for the organization. A departing employee or an external service provider should not be able to avoid the policy simply because the account or device is not company-owned.
Define “AI system” broadly. Include public chatbots and copilots, enterprise assistants, model APIs, document and meeting summarizers, transcription services, image and audio generators, code-completion tools, recruiting or analytics features, and AI embedded in ordinary business software. A feature can create the same privacy and confidentiality exposure even when it is not marketed as a standalone AI product.
Cover company devices, managed accounts, bring-your-own-device access to company data, and personal accounts used for work. State whether experimentation with non-work data is allowed and prohibit employees from moving company material to a personal account to bypass controls.
How should employees classify data before prompting?
Map the organization’s existing information classes to explicit AI handling rules. If the organization has no classification scheme, adopt one before broad rollout; a simple four-level model is easier to enforce than an ambiguous “use common sense” rule.
| Data class | Examples | AI handling rule | Required approval |
|---|---|---|---|
| Public | Published website text, public filings, approved marketing copy, information already released publicly | May be used in an approved tool for an approved business purpose. Confirm that combining several public items does not reveal a confidential inference. | Normal manager or workflow approval; follow the tool’s listed settings. |
| Internal or low-risk | Routine internal procedures, non-sensitive drafts, generic project notes | Use only an approved tool that has documented retention, access, and reuse controls. Remove unnecessary names and identifiers. | Business owner approval where the register requires it. |
| Confidential or proprietary | Unreleased financials, source code, product roadmaps, customer contracts, pricing, trade secrets, merger plans | Do not enter into public or unapproved AI services. Permit only in a specifically approved environment with contractual, technical, privacy, and security safeguards. | Written approval from the data owner plus security, privacy, and legal review when applicable. |
| Personal or regulated | Employee, customer, patient, student, or applicant records; identifiers; health, biometric, payment, or other legally protected data | Default to prohibition unless a documented lawful basis, minimization plan, security assessment, retention limit, and approved processing arrangement exist. | Privacy and legal approval, data-owner approval, and any sector or jurisdiction review required by law. |
| Security-sensitive | Passwords, API keys, tokens, private certificates, vulnerability details, incident evidence, security architecture | Never paste credentials or secrets. Restrict other security material to a controlled tool and workflow designed for it; redact or synthesize examples where possible. | Security-owner approval and a documented handling procedure. |
Classification applies to prompt text, attachments, conversation history, generated files, and feedback used to improve a model. Employees should assume that a provider can retain or review content until the approved register says otherwise.
Which AI tools may employees use?
Maintain an approved-tool register rather than publishing a one-time list that becomes obsolete. Each entry should identify a business owner, technical owner, permitted use cases, data classes, account type, regions or hosting locations, retention and deletion behavior, model-training or reuse terms, access controls, encryption, logging, subprocessors, support access, and contract terms.
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Record the date of the last review, the next review date, and a status such as approved, restricted, pilot, suspended, or prohibited. A tool may be approved for public text summarization but restricted for source code or personal information; approval is not automatically transferable between use cases.
Vendor due diligence should test more than marketing claims. The Office of the Australian Information Commissioner advises organizations to assess testing, oversight, privacy, security, and access before putting personal information into an AI system. Require evidence of those controls, a breach-notification commitment, deletion options, subprocessor disclosure, and a way to disable training on customer content where relevant.
| Register field | Question to answer |
|---|---|
| Tool and version | What exact product, edition, model, plug-in, and account configuration are covered? |
| Use case | What task is approved, and what tasks are explicitly outside scope? |
| Permitted data | Which classification may be entered, and what must be redacted? |
| Provider controls | How are retention, deletion, training reuse, encryption, identity, logging, and subprocessors handled? |
| Governance | Who owns the tool, approves changes, handles incidents, and confirms renewal? |
| Exit plan | How will data, prompts, logs, and integrations be exported or deleted if the tool is replaced? |
Block or quarantine tools that have no accountable owner, cannot meet the organization’s data requirements, or materially change their processing terms without notice.
How should a team approve an AI use case?
Evaluate each proposal on the same axes so that approval does not depend on who asks first. A low-risk drafting task can move quickly; a system that influences a person’s eligibility, employment, treatment, or access to essential services needs a much higher threshold.
| Risk axis | Lower-risk indicator | Higher-risk indicator |
|---|---|---|
| Data sensitivity | Public or synthetic information | Personal, regulated, confidential, trade-secret, or security data |
| Tool trust | Contracted service with tested controls and restricted access | Personal account, unclear retention, broad provider reuse, or unknown subprocessors |
| Use-case impact | Brainstorming or formatting with human editing | Employment, credit, health, legal, safety, disciplinary, or customer eligibility decisions |
| Human oversight | Qualified reviewer checks every output before use | Automated action or output sent directly to a person or production system |
| Employee competence | Trained user operating within a documented procedure | Untrained user or a novel task with no review standard |
| Monitoring and response | Logs, owner, testing, and a clear reporting route | No audit trail, no owner, or no way to stop the workflow |
| Jurisdiction and sector | Routine internal work with known obligations | Cross-border processing or a regulated sector with additional duties |
Use the highest applicable risk level, not an average. Approval should specify the exact prompt or workflow boundary, the reviewer, the retention period, and the conditions that trigger reapproval.
What uses should be prohibited or restricted?
Write prohibitions as observable actions. Employees should not:
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- Enter passwords, API keys, private certificates, authentication codes, or other credentials into any AI tool.
- Upload personal, confidential, regulated, or proprietary data to an unapproved public service or personal account.
- Disable security controls, evade monitoring, or ask an AI system to bypass an access restriction.
- Use generated text, images, audio, or code to impersonate, deceive, harass, discriminate, or create unlawful content.
- Represent unverified generated material as factual, original, legally cleared, or human-authored when that distinction matters.
- Allow an AI system to make a consequential decision without a qualified human review and an appeal or correction path.
Restrict high-impact workflows even when the data is not obviously sensitive. Require documented testing for disparate impact, an explanation of the model’s role, human sign-off, records of the inputs and output used, and a way for an affected person to request correction. Legal and sector-specific requirements may add notice, consent, documentation, or audit duties.
What human review is required before using an AI output?
The person who publishes, sends, executes, or relies on an output remains responsible for it. Review should be proportionate to the harm a mistake could cause, not merely to the length of the generated response.
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- Check scope and currency. Confirm that the answer addresses the actual question, reflects current policy or law, and does not silently invent dates, figures, citations, or capabilities.
- Check privacy and confidentiality. Remove unnecessary personal details, secrets, and internal context from the output before sharing it.
- Check fairness and suitability. Look for stereotypes, discriminatory assumptions, inaccessible language, unsafe instructions, and recommendations that exceed the user’s authority.
- Check rights and provenance. Review copyright, licensing, confidentiality obligations, and whether generated code or media can be used in the intended setting.
- Record the decision. For a high-impact or regulated use, retain the reviewer, date, tool and model version, relevant inputs, output relied upon, and corrections made, subject to applicable retention rules.
Do not treat a confident tone, citation-like formatting, or a high model score as proof of accuracy.
How should employees be trained?
Training should be role- and risk-specific rather than a single annual video. EU AI Act Article 4 requires providers and deployers to take measures supporting staff AI literacy; the requirement applied from 2 February 2025. European Commission FAQs explain that there is no one-size-fits-all format and that organizations may keep internal training records.
Core training for everyone
- What the policy covers and how to find the approved-tool register.
- How to classify data and remove identifiers before prompting.
- Why public tools may retain, expose, or reuse submitted content.
- Common failure modes: hallucinated facts, stale information, prompt injection, data leakage, bias, and insecure code.
- How to write safe prompts without including unnecessary business context.
- How to review, label, and cite AI-assisted work.
- How and where to report a suspected incident.
Additional modules by role
- Developers: secret handling, dependency and license checks, code review, sandboxing, and testing generated code.
- Managers and analysts: decision accountability, bias checks, documentation, and escalation for high-impact uses.
- Privacy, security, legal, and procurement teams: vendor assessment, contracts, threat modeling, records, and regulatory analysis.
- Executives and tool owners: risk acceptance, monitoring results, budget, incident decisions, and change management.
Use short exercises based on real workflows, such as spotting a hidden identifier in a prompt or correcting a plausible but false summary. Keep completion, assessment, role, and refresher dates in a training record.
Who is accountable, and what records should be kept?
Name a policy owner with authority to interpret the standard and stop unsafe use. Give every approved tool a business owner and technical owner, and identify privacy, security, legal, records, procurement, and data owners who must be consulted for the relevant risk level.
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Maintain an AI-use-case inventory containing the purpose, users, data classes, tool and model, jurisdiction, impact assessment, approval decision, controls, reviewer, start date, next review, and retirement date. Keep vendor assessments, contracts, training records, test results, exceptions, incidents, and remediation decisions. Access to logs should follow least privilege because prompts and outputs can themselves contain sensitive information.
NIST’s Generative AI Profile, NIST AI 600-1 (2024), recommends documented roles, communication lines, incident procedures, and trained incident responders. The broader NIST AI Risk Management Framework 1.0 (2023) is intended for voluntary use, but its governance, mapping, measurement, and management structure is a useful way to organize internal controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should happen when an employee reports an AI incident?
Make reporting easy: provide one channel for urgent security or privacy events and a normal channel for suspected policy violations, with anonymous reporting where appropriate. Tell employees not to investigate beyond their authority or delete evidence.
- Contain. Stop the workflow, revoke exposed credentials, disable sharing, disconnect integrations, or suspend the tool account as appropriate.
- Report and escalate. Notify the policy owner, security, privacy, legal, and the affected business owner according to the severity matrix.
- Preserve evidence lawfully. Record the tool, account, time, prompts, files, recipients, settings, and relevant logs while respecting privacy and retention rules.
- Assess impact. Determine what data or decisions were affected, whether a provider or subprocessor received it, and whether notification obligations apply.
- Remediate. Correct or retract outputs, notify affected people when required, reset secrets, close the technical gap, and provide support to users.
- Learn and update. Document root cause, contributing conditions, decision owners, and changes to the register, training, contracts, or controls.
CMS responsible-use guidance requires reporting suspected or verified privacy breaches or unintended disclosures. Your policy should set internal deadlines that are faster than any external notification deadline.
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How often should the policy and approved tools be reviewed?
Conduct a formal review at least annually and whenever there is a material change: a new model or feature, altered provider terms, a new jurisdiction, a data-classification change, a serious incident, or a high-impact use case. Tool owners should monitor changes between formal reviews.
Review whether retention and deletion still operate as documented, whether access remains appropriate, whether model behavior or connected data sources changed, and whether employees are using shadow tools. Sample approved workflows for accuracy, bias, privacy leakage, and unsafe actions. Retire entries that have no current owner or business need.
Track practical indicators such as overdue reviews, unapproved-tool detections, training completion, incidents by cause, time to contain, and recurring failure modes. These measures support corrective action; they are not a substitute for examining individual high-risk decisions.
How can an organization implement the policy in 90 days?
- Days 1–15: establish ownership. Appoint the policy owner, create the cross-functional review group, inventory current AI use, and freeze new high-risk deployments until controls exist.
- Days 16–30: map data and workflows. Confirm classification labels, identify regulated information, list consequential decisions, and publish a temporary rule prohibiting sensitive data in unapproved public tools.
- Days 31–45: build the register. Assess the tools already in use, document permitted data and settings, negotiate missing contract terms, and mark each entry approved, restricted, pilot, suspended, or prohibited.
- Days 46–60: write procedures. Add approval forms, human-review checklists, exception criteria, incident playbooks, retention rules, and technical controls such as identity management, logging, data-loss prevention, and secret scanning.
- Days 61–75: train and pilot. Deliver core and role-specific training, run a small number of low-risk use cases, test refusal and escalation paths, and collect user feedback.
- Days 76–90: launch and measure. Publish the policy and register, require acknowledgement, begin monitoring, review pilot evidence, and schedule the first formal review and audit.
Do not make speed the only success measure. A slower approval that prevents an irreversible disclosure is preferable to an uncontrolled rollout that creates legal, security, or trust costs.
What should the policy say about exceptions?
Allow exceptions only when a named approver accepts the documented residual risk for a defined period. An exception request should state the purpose, data, tool, users, safeguards, legal basis where relevant, start and end dates, reviewer, and exit plan. Never use an exception to permit credentials or to bypass a mandatory legal prohibition.
Automate expiry reminders and require renewal after a material change. Record denied requests as well as approvals so the organization can identify recurring needs and improve the approved-tool catalogue.
Frequently Asked Questions
Can employees paste company data into ChatGPT?
Only when the exact account, configuration, data class, and business use are approved. A public or personal account should be treated as unapproved unless the register documents contractual, retention, access, and security protections that permit the data.
What information should never go into an AI tool?
Never enter passwords, API keys, private certificates, or authentication codes. Unless a specifically controlled workflow authorizes it, keep personal, regulated, trade-secret, confidential, incident, and other security-sensitive information out of public or unapproved services.
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Do small businesses need a formal AI approval committee?
They need accountable ownership and documented decisions, but not necessarily a large committee. One person can coordinate approvals while consulting privacy, security, legal, and business owners whenever the data or use case requires their expertise.
How can we detect employees using unapproved AI services?
Combine clear training with technical signals such as identity-provider logs, DNS or secure web-gateway events, data-loss-prevention alerts, browser or endpoint controls, and procurement reviews. Explain monitoring in the policy, limit access to authorized staff, and provide a safe route for employees to request a tool instead of hiding experimentation.
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