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Use a clear, risk-based route instead of requiring permission for every prompt. Give employees a short list of approved AI services and routine uses, define exactly what data each service may receive, require human checking before important outputs are used, and provide an escalation path for sensitive or novel workflows. This lets low-risk drafting and search assistance move quickly while directing personal data, confidential information, and worker-impacting decisions to privacy, security, legal, HR, or another accountable owner.
The approach below is an operational synthesis of guidance from NIST, France’s CNIL, the EU AI Act, and UK ICO guidance. It is not a universal legal checklist: obligations depend on your country, sector, contracts, data, and the AI system’s function.
What does risk-based AI governance mean for an employer?
Risk-based governance means the control gets stricter as the potential harm increases. An employee asking an approved assistant to improve the wording of a public webpage is not the same as a system that ranks applicants, analyzes worker behavior, or processes health information. Treating both as identical either blocks useful work or leaves consequential uses under-controlled.
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Build the policy around four questions:
- What is the employee trying to accomplish?
- What data will the system receive, retain, or send to another provider?
- Could an error affect a person, money, safety, legal rights, or a regulated obligation?
- Who is accountable for checking the result and responding if it fails?
These questions are more durable than a list of model names. A tool’s risk can change when its settings, provider terms, connected data, or use case changes.
How can we create an approved path that does not slow routine work?
Publish a small, understandable internal policy or charter that names approved services, allowed work, prohibited work, data boundaries, and an exception route. CNIL specifically recommends internal policies or charters that clearly define authorized and prohibited uses. See CNIL’s Q&A on generative AI.
Start with use-case tiers
Use tiers to determine the amount of friction. The labels below are an implementation pattern, not a statutory classification.
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| Tier | Typical work | Default route | Minimum control |
|---|---|---|---|
| Routine, low impact | Brainstorming with public facts, rewriting a non-sensitive draft, summarizing public material, translation of non-confidential text | Self-service in an approved service | Follow the data rule; employee checks the result before publishing |
| Internal, moderate impact | Summarizing ordinary internal documents, drafting internal communications, extracting themes from non-sensitive project notes | Approved service configured for company use | Access control, retention settings, output review, and a named business owner |
| Sensitive or regulated data | Personal data, customer records, employee files, health or financial information, credentials, trade secrets, or material subject to a legal hold | Approval before use, or a specifically authorized environment | Privacy and security review of deployment, provider terms, access, retention, and transfer obligations |
| Consequential or novel workflow | Recruiting or promotion recommendations, worker monitoring, decisions about pay or discipline, safety or eligibility decisions, automated actions in business systems | Formal review by the relevant owner before a pilot or production use | Documented purpose, testing, human authority to override, monitoring, records, and a route to challenge outcomes |
Put the low-risk route first in the policy. An employee should be able to answer “Which approved service may I use for this ordinary task?” without opening a ticket. The escalation route should be equally concrete: a form or channel, required information, expected reviewer, and what work may continue while approval is pending.
Make approval about the workflow, not just the brand
Approving a service for public brainstorming does not automatically approve its plug-ins, browser extensions, connectors, or use with confidential files. Record the approved purpose, configuration, data classes, user group, provider, contract, retention behavior, and owner. If any of those changes, re-evaluate the approval.
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What information can an employee put into an AI tool?
There is no universal “safe for AI” data category. Whether information may be entered depends on its sensitivity, the deployment model, provider terms, reuse settings, access controls, and your legal obligations. CNIL notes that restrictions depend on how the system is deployed and whether provider reuse is possible; an on-premises deployment may make provider reuse infeasible, but it does not remove other privacy, security, accuracy, or retention duties.
| Data type | Default employee rule | What to verify before allowing it |
|---|---|---|
| Public information | Allowed in approved services for routine tasks | Check that the prompt does not add confidential context and that the output is reviewed for errors |
| Routine internal information | Use only the service approved for internal material | Identity and access controls, provider reuse and retention, encryption, regional processing, and deletion terms |
| Personal data about customers or workers | Do not paste into a general-purpose service unless the workflow has been approved | Lawful basis, purpose limitation, minimization, processor terms, transfers, retention, access, and rights handling |
| Credentials, secrets, security findings, or regulated records | Prohibited by default; use a specifically authorized environment if one exists | Security architecture, segregation, logging, incident response, and applicable sector or contractual rules |
| Confidential business material and trade secrets | Use only where the owner has approved the service and configuration | Provider’s training or reuse terms, tenant isolation, download and sharing controls, and retention |
State the boundary in examples employees recognize: “You may ask the approved assistant to summarize the internal project plan; do not paste a customer export, employee medical note, password, or unreleased acquisition document unless the designated workflow explicitly permits it.” Avoid relying on a setting described only as “private” or “enterprise”; have procurement, security, and privacy confirm what that setting actually does.
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Remove names, identifiers, free-text comments, account numbers, and unnecessary rows. Use a synthetic or redacted sample to test a prompt. If the task genuinely requires identifiable data, document why, limit access to the people who need it, and use the approved environment rather than bypassing controls.
When should an employee ask for approval?
Ask before use whenever the answer to any of these checks is yes:
- The tool is not on the approved list, or the employee wants to add a plug-in, connector, extension, or automated action.
- The prompt contains personal, confidential, regulated, security-sensitive, or contract-restricted information.
- The system will make a recommendation, ranking, classification, or action affecting a worker, customer, applicant, supplier, account, benefit, price, safety outcome, or legal right.
- The output will be sent externally, published as authoritative, used in a filing, or used to operate a production system.
- The workflow creates a new data store, transfers information to another country, or changes provider retention or reuse.
- The employee cannot explain who will verify the output, what happens when it is wrong, or who owns the decision.
Route the question to the owner that can resolve it: security for access and threats, privacy or legal for personal data and regulatory duties, HR for worker decisions, procurement for provider terms, and the business owner for process and quality. One intake form can triage to several owners; employees should not have to guess which department to contact.
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What to include in an exception request
- Describe the business outcome and why an AI system is needed.
- Name the service, provider, model or feature, integrations, and intended users.
- List the data classes, geographic locations, retention, and whether provider reuse or training is possible.
- Explain who may be affected and whether the output informs or takes a consequential decision.
- Define testing, human review, override authority, monitoring, incident handling, and a stop condition.
- Identify the accountable business owner, security or privacy reviewers, decision date, and review date.
How should people check AI output before relying on it?
Human responsibility must be real, not a final click on an automatically generated recommendation. For important work, the reviewer needs enough context, time, competence, and authority to reject or change the output.
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- Accuracy: Check names, numbers, citations, dates, calculations, and claims against authoritative records.
- Completeness: Look for omitted exceptions, minority cases, or instructions the system misunderstood.
- Confidentiality: Confirm that the output did not reveal data to an unintended audience.
- Fairness and relevance: Test whether irrelevant characteristics or historical bias affected a recommendation.
- Actionability: Ensure a person with appropriate authority approves any external communication or system action.
- Traceability: Keep the prompt, source material, output, edits, and decision record when the process requires an audit trail.
For ordinary drafting, the employee may perform this check. For a decision about a worker or another significant interest, assign a reviewer who can examine other information and depart from the automated recommendation. The UK ICO describes meaningful human influence in relevant UK GDPR contexts as including the ability and authority to intervene or challenge a solely automated result. Read the ICO guidance on automated worker-monitoring decisions.
How should an organization choose an approved AI service?
Evaluate the service against the actual workflow rather than choosing solely on output quality or popularity. Record evidence for each decision so a later change in provider terms or configuration triggers a review.
| Evaluation area | Questions to answer |
|---|---|
| Data handling | What is collected, stored, logged, shared, or used to improve the service? Can the organization set retention, deletion, region, and tenant isolation? |
| Administration | Does it support single sign-on, role-based access, provisioning and deprovisioning, audit logs, rate limits, and connector controls? |
| Work fit | Does it handle the required languages, formats, context length, accessibility needs, and integrations without unsafe workarounds? |
| Quality and verification | Can users inspect sources, preserve versions, reproduce a result, and test known failure cases? |
| Human review and records | Can reviewers see inputs and outputs, record edits and decisions, and stop an automated action? |
| Legal and contractual fit | Where is processing performed? Which terms govern confidentiality, intellectual property, subprocessors, transfers, incidents, and deletion? |
| Employee effort | Does the approved route save time compared with an unsanctioned workaround, and can employees get help quickly? |
Do not describe an approval as permanent. Recheck it when the provider changes terms, model behavior, data sources, connectors, user population, or the business purpose.
What training do employees and managers need?
Training should match the systems and tasks people actually perform. The EU AI Act requires providers and deployers to take measures to ensure sufficient AI literacy for staff and others operating systems on their behalf, taking context and the people involved into account. It does not prescribe one universal course or schedule. Read Regulation (EU) 2024/1689.
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CNIL also advises appropriate training and regular checks. See CNIL’s recommendations. A practical program includes:
- A short orientation on approved tools, prohibited data, provider reuse, and how to report an incident.
- Role-specific exercises: drafting for communications staff, data minimization for analysts, and review of recommendations for managers.
- Prompt and output-verification practice using realistic, non-sensitive examples.
- Scenarios showing the difference between writing assistance and a system that influences a person’s employment, access, pay, or discipline.
- Manager training on when a “human review” is insufficient and how to document a decision.
- Refreshers after a material tool, policy, legal, or workflow change, plus periodic checks that people follow the boundary.
Measure completion and understanding with a brief scenario-based assessment, not just attendance. Give employees a simple reporting channel for a suspected data exposure, harmful output, or policy gap.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What legal duties apply to workplace AI?
Legal scope is jurisdiction-specific. Confirm the current text, effective dates, system classification, collective-bargaining obligations, sector rules, and local employment and privacy law before deploying a workflow.
European Union
The EU AI Act includes AI-literacy duties for providers and deployers. Additional deployer duties described by the European Commission apply to high-risk systems. For workplace deployment, the Commission says affected employees and workers’ representatives must be informed beforehand. The Act also prohibits emotion-recognition systems in workplaces except for medical or safety reasons. Consult the European Commission’s AI Act FAQ and the regulation’s current legal text.
United Kingdom
UK ICO guidance addresses worker monitoring and solely automated processing. For decisions with legal or similarly significant effects, it discusses informing workers, providing ways to request human intervention or challenge a decision where applicable, and ensuring oversight is meaningful. That guidance is UK-specific and does not automatically govern employers elsewhere. Review the ICO guidance.
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The European Data Protection Supervisor’s 2025 generative-AI guidance is directed at EU institutions, bodies, offices, and agencies under their data-protection framework. It is a useful reference, not binding guidance for every European employer. Read the EDPS guidance.
How should we document, monitor, and update governance?
Keep a lightweight AI register for approved and piloted workflows. For each entry record the purpose, owner, service and provider, users, data classes, configuration, review decision, controls, incident contact, and next review date. This creates accountability without asking every employee to write a risk assessment for a routine prompt.
Monitor signals that justify action
- Repeated reports of fabricated facts, biased recommendations, or unexplained changes in output quality.
- Attempts to paste prohibited data or use unapproved connectors.
- Provider changes to retention, training, subprocessors, regions, or model behavior.
- New laws, regulator guidance, contracts, collective agreements, or customer requirements.
- Incidents, near misses, complaints, access anomalies, or a workflow expanding beyond its original purpose.
Use access logs and sampling where proportionate; do not collect more employee content than needed to administer security and quality. When a material issue appears, pause the workflow, preserve relevant records, notify the responsible owners, and decide whether to remediate, narrow, or retire it.
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- Inventory current use. Ask teams which tools, data, prompts, plug-ins, and automated actions they already use. Include sanctioned and unsanctioned workarounds.
- Set the minimum policy. Publish approved services, routine uses, prohibited data, review expectations, incident reporting, and the exception channel in plain language.
- Configure the safe route. Apply identity, access, retention, region, connector, logging, and deletion settings before inviting broad use.
- Pilot representative tasks. Test public drafting, internal summarization, and at least one proposed sensitive or worker-impacting workflow with realistic failure cases.
- Train and launch. Give role-based examples, a quick reference, and a staffed help route. Tell employees what they can do immediately without permission.
- Review evidence. After launch, inspect incidents, support questions, quality samples, access records, and changed provider terms. Adjust tiers and controls instead of adding blanket bans.
A successful launch is not “everyone may use any chatbot.” It is a predictable path in which routine work is fast, sensitive work is visible to the right reviewers, and someone remains accountable for every consequential outcome.
What common governance mistakes should we avoid?
- One blanket ban: Employees may move work to hidden tools, while low-risk use receives no safe alternative. Provide approved options and explain the boundary.
- A brand-only allowlist: The same service can be safe for public text and unsafe for a customer database. Approve a configured use case and data class.
- “Human in the loop” as a rubber stamp: A reviewer without time, information, or authority cannot provide meaningful oversight. Give the reviewer power to investigate and override.
- Vague data language: “Do not share sensitive data” is hard to apply. Name public, internal, personal, credential, regulated, and confidential examples.
- Ignoring connected features: A plug-in or connector can change what the service can access and do. Review integrations separately.
- Training once and forgetting: Models, terms, regulations, and workflows change. Schedule checks and refresh training after material changes.
- Overclaiming legal coverage: EU, UK, and other jurisdictions differ, and not every assistant is a high-risk or solely automated system. Have qualified local owners confirm scope.
Frequently Asked Questions
Can employees use AI for brainstorming without asking permission?
Usually yes when the service is approved, the prompt uses public or otherwise permitted information, and a person checks the result. Do not let a brainstorming label conceal personal data, confidential strategy, or an automated action.
Does using an enterprise or private AI setting make confidential data safe?
Not automatically. Confirm the actual retention, provider-reuse, access, region, deletion, connector, and contractual terms for the configured service, then apply your organization’s privacy and security requirements.
What records should we keep for an AI-assisted decision?
Keep the purpose, system and version or feature, relevant inputs and outputs, reviewer and edits, evidence considered, final decision, and any challenge or correction. Retain only what your legal, privacy, and records schedules require.
Should we disclose AI assistance to customers or employees?
Disclosure depends on the use, jurisdiction, contract, and whether a person is affected by an automated or high-impact process. Have legal, privacy, HR, or the accountable business owner set the notice requirement for each workflow rather than adopting one unsupported global rule.
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