The Tool Desk
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There is no universal policy that guarantees compliance everywhere. Requirements vary by jurisdiction, sector, data, business purpose and the consequences of a particular use. Use recognized frameworks as adaptable guidance, and have the people responsible for legal, privacy, security and risk check the final policy against your team’s circumstances.
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Start with a policy that answers five practical questions
Before writing clauses, make sure the document lets an employee answer these questions without guessing:
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- Which AI tools, systems, people and work activities are covered?
- What may I do without approval, what needs a documented review, and what is prohibited?
- What information may I enter, and which information must stay out of an AI system?
- Who checks an output, approves a higher-risk use, and receives a concern or incident report?
- When will the policy change, and how will I learn about the change?
If a rule cannot be applied at the moment someone is choosing a tool or uploading data, rewrite it in plainer language or add an example. A short, specific rule is more useful than a broad statement such as “use AI responsibly.”
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Define scope, purpose and terms
List what is in scope
State whether the policy covers only generative chat tools or also includes coding assistants, image and audio generators, transcription services, automated decision systems, embedded AI features in ordinary software, internally developed models and systems supplied by a vendor. Include systems already deployed and third-party systems used on the team’s behalf. Explain whether contractors, temporary staff, interns, suppliers and customer-facing operations must follow it.
Scope should cover the work, not just a shopping list of applications. A tool can add a new AI feature after the policy is published, and an employee can use a personal account for company work. Define covered activity broadly enough to catch both situations while allowing the policy owner to maintain an inventory of named systems.
State the purpose
Explain what the organization is trying to achieve: for example, useful experimentation with controlled exposure to privacy, security, accuracy, intellectual-property and operational risks. The purpose should make clear that productivity is not the only objective; protecting people and maintaining accountable decisions matter as well.
Define terms in ordinary language
Define “AI system,” “generative AI,” “input,” “output,” “personal data,” “confidential information,” “high-impact decision,” “human review,” “approved tool” and “incident.” Use the definitions your privacy, security and legal documents already use where possible. Consistent language prevents an employee from treating a tool as exempt simply because its marketing calls it an assistant, search feature or automation.
Classify uses by risk and approval need
Do not make every use follow the same process. A brainstorming prompt containing no sensitive information has a different risk profile from a system that summarizes health information or recommends an employment decision. Your policy can use three categories, with the final examples adapted to your work:
Permitted uses
These are low-impact activities using an approved system and information cleared for that system. Illustrative examples include generating ideas, improving the clarity of a draft that contains no confidential material, creating a first-pass outline, or converting public information into a different format. The employee remains responsible for checking the result before using it.
Restricted uses requiring review or approval
Put a use in this category when an error, disclosure or unfair outcome could materially affect a person, customer, legal position, security posture or business operation. Examples include processing personal or customer information, producing content for a regulated process, generating code that will run in production, ranking job candidates, or making a recommendation that affects access to a service. Require the named reviewer or approver to document the system, purpose, data, expected impact, safeguards and decision.
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Prohibited uses
Prohibit uses that your organization cannot justify or control. Depending on context, that may include entering secrets into an unapproved service, presenting unverified generated text as a factual record, allowing an AI output to make a high-impact decision without the required human process, bypassing security controls, or using a system for a purpose that privacy, legal or risk owners have rejected. Have the relevant owners validate the prohibited list; a blanket ban can accidentally drive work into personal accounts where it is harder to monitor.
Record the decision axes
When deciding a category, assess the sensitivity of the data, the potential impact of an error, the degree of human review, who owns the decision and escalation, how well the use fits existing controls, and the applicable jurisdiction or sector requirements. Record the reasoning for borderline cases so a later reviewer can understand why a use was approved.
Set clear rules for data and confidentiality
Use an information classification rule
Link the AI policy to the organization’s data-governance and security classifications. Identify which classes may be entered into which approved systems, which require written approval or technical safeguards, and which may never be entered. The rule should cover prompts, uploaded files, connected drives, plug-ins, retrieved documents, conversation history, generated outputs and telemetry—not only the text typed into a chat box.
Address personal, customer and regulated data
Tell staff to minimize data, remove unnecessary identifiers, use the least amount of information needed for the task and follow the organization’s retention and deletion rules. Explain whether a vendor may retain prompts for service improvement, whether administrators can inspect conversations, where processing occurs, and how access is removed. Those details must come from the approved tool’s terms and your own controls; do not assume that a paid account, private workspace or “enterprise” label answers them.
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Call out source code, credentials, security findings, unpublished financial information, contract terms, customer records, employee files, merger information and other categories your organization treats as confidential. Secrets such as passwords, access tokens, private keys and recovery codes should never be pasted into an AI prompt. Provide a safe alternative, such as a redacted sample, an approved internal system or a manual process.
Control outputs as well as inputs
An output can contain personal information, a copied passage, a vulnerability or an invented statement even when the prompt did not. Apply the same classification, access, retention and sharing rules to generated material that you apply to other work product. Require employees to label or record AI assistance when your professional, contractual or regulatory process requires it.
Make human review specific and testable
Define what the reviewer checks
“A human is in the loop” is not a control unless the person has time, authority and relevant knowledge. Require the reviewer to check factual accuracy, completeness, relevance, bias or unfair treatment, privacy and confidentiality, security implications, intellectual-property concerns and compliance with the purpose for which the output was generated.
Match review depth to impact
For a low-impact draft, a quick accuracy and tone check may be enough. For code, customer communications, financial analysis, safety-related material or a recommendation affecting an individual, require a qualified person to validate the result against source information and the applicable procedure. For high-impact decisions, specify that the authorized human decision-maker—not the model—makes and records the decision, with a way to correct an error or challenge an outcome.
Keep evidence of review
Specify what must be recorded: the tool and version or workspace, date, purpose, important inputs, reviewer, checks performed, changes made and final disposition. Do not demand a transcript for every harmless brainstorming session if that would create unnecessary sensitive-data stores. Set a retention period that matches existing records rules and the risk of the use.
Assign ownership, approval and escalation
Employees need to know who can make a decision before they adopt a tool. Define at least these roles and give each a named team, function or job title:
| Role | What the policy should assign |
|---|---|
| Policy owner | Maintains the document, coordinates reviews, publishes changes and keeps the approved-tool or use register current. |
| Business or use-case owner | Describes the purpose, expected benefit, users, data, impact and operating process for a specific use. |
| Privacy and legal reviewers | Assess personal data, contractual duties, intellectual-property questions and requirements that apply to the use. |
| Security and technical reviewers | Assess access, integration, logging, model or vendor behavior, data flows, vulnerabilities and operational safeguards. |
| Risk or ethics reviewer | Examines material harms, fairness, affected people, controls and residual risk where the organization uses such a function. |
| Exception approver | Accepts or rejects a documented deviation, sets conditions and records an expiry or reconsideration date. |
| Incident contact | Receives reports, starts triage, coordinates containment and routes the matter to privacy, security, legal or operational response teams. |
Document the chain of command for disagreements. A policy that names five reviewers but no final decision-maker leaves employees unable to act and encourages informal workarounds.
Use a repeatable approval workflow
For a new tool or a material change in use, give staff a simple path:
- Describe the use. Record the business purpose, users, affected people, expected benefit, decision being supported and whether the system is internal or supplied by a third party.
- Map the data flow. Identify every input, output, connector, administrator, storage location, transfer and retention point. Classify the data before testing.
- Assess impact and failure modes. Ask what happens if the output is wrong, biased, unavailable, disclosed or manipulated. Consider both ordinary errors and deliberate misuse.
- Choose safeguards. Select access restrictions, redaction, approved environments, validation tests, monitoring, human sign-off, fallback procedures and user notices that fit the risk.
- Obtain the right reviews. Route the record to privacy, legal, security, technical, risk or other owners required by the category. Keep their conditions with the approval.
- Run a controlled pilot. Test with representative but appropriately protected data. Compare outputs with authoritative sources and record failures, corrections and unresolved limitations.
- Approve, reject or narrow the use. State the permitted purpose, users, data, system, review requirement, expiry or reapproval trigger and person accountable for operation.
- Monitor in production. Check incidents, quality, access, drift, user complaints, unexpected outputs and whether people are following the procedure.
- Reassess after change. Reopen the review when the tool, model, vendor terms, data, audience, purpose, integration, impact or applicable requirement changes.
Connect the policy to existing controls
An AI policy should point to the controls that already govern your organization instead of creating a parallel rulebook. Cross-reference privacy impact assessments, records retention, access management, secure-development practices, procurement, third-party risk, incident response, accessibility, intellectual-property review, information classification and business continuity. State which document wins when requirements appear to conflict, and provide one route for questions.
For vendor systems, require procurement or the designated owner to examine data use, security commitments, subprocessors, service location, deletion, availability, support, audit evidence and change notifications. The policy can require an approved contract or assessment without promising that a vendor’s feature is safe by default.
Plan incident reporting and response
Define reportable events
Examples include sending protected information to an unapproved system, receiving a suspicious or malicious output, discovering a material factual error after publication, exposing credentials, bypassing a required review, discriminatory or harmful behavior, unauthorized access, or a vendor change that affects an approved use. Include near misses when they reveal a control weakness.
Make reporting easy
Provide a single channel or service route, identify what information to include, and say that employees should report promptly rather than investigate beyond their role. Ask for the tool, time, purpose, data involved, people affected, output or evidence, actions already taken and the safest way to contact the reporter. Avoid telling staff to paste sensitive material into an ordinary ticket if the ticketing system is not approved for it.
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Assign triage, containment, preservation of evidence, notification, remediation, recovery and lessons-learned owners. Link the AI process to existing privacy and security incident procedures so statutory or contractual deadlines are not overlooked. Test the route with a tabletop exercise; an untested escalation tree is not a reliable control.
Train people with examples they recognize
Training should be role-based and short enough to use. Everyone needs the scope, permitted and prohibited patterns, data rules, review duty and reporting route. Developers need secure integration, testing, logging and dependency guidance. Managers need approval, performance and accountability expectations. Reviewers need a method for checking accuracy, bias, privacy, security and provenance. Procurement and administrators need vendor, access and retention controls.
Show paired examples rather than abstract warnings:
- Safer pattern: ask an approved tool to improve the wording of a public announcement, then have the owner verify the final text.
- Unsafe pattern: paste an unredacted customer record into a personal account to obtain a summary.
- Safer pattern: use synthetic or redacted data to test a coding assistant and run the normal security review before deployment.
- Unsafe pattern: accept a generated eligibility or hiring recommendation without the required authorized human assessment.
Tell employees where to ask a question and publish a current list of approved tools or uses. Record completion, refresh training after material changes and give managers a way to identify work that needs a deeper review.
Set review dates and change triggers
Choose a normal review cadence appropriate to your risk, then add immediate triggers. At minimum, reopen the policy or affected use after a new tool, model, connector or vendor; a material incident or near miss; a changed purpose, audience, data set or impact; a change in law, contract or sector requirement; a security finding; or evidence that employees cannot apply the rule.
Version the document, record who approved each change, publish an effective date and archive superseded copies. Notify affected users directly when a rule changes. Keep the policy owner responsible for checking that linked procedures, training and tool registers still match the text.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use NIST and OECD guidance without turning it into a checklist
NIST describes the AI Risk Management Framework as voluntary. Its Playbook suggests actions organized around Govern, Map, Measure and Manage, including scope, responsibilities, data, review, monitoring, change management, third-party systems and incident response. The Playbook states: “The Playbook is neither a checklist nor set of steps to be followed in its entirety.” Select the actions that fit your context and explain why a control is proportionate.
NIST’s framework overview records that AI RMF 1.0 was released on January 26, 2023, the Generative AI Profile on July 26, 2024, and that AI RMF 1.0 is being revised. The same overview records an April 7, 2026 concept note for a critical-infrastructure profile. Check the live NIST material when you update your policy rather than treating any version as permanent.
The OECD’s 2025 report describes a generative-AI policy as setting acceptable-use terms and establishing safeguards and oversight. It also discusses organizational guidance and workforce AI literacy. Examples cited for a federal-government setting are not automatically obligations for a private team or an organization in another jurisdiction; use them as ideas, then obtain the reviews your context requires.
Tailor the final text to your organization
Before publication, ask the owners of legal, privacy, security, risk, data governance, procurement, records, accessibility and the relevant business processes to review the draft. Confirm the geography of processing, sector rules, customer and supplier commitments, employment implications, affected groups, languages, accessibility needs and the actual tools employees use. If the team operates across jurisdictions, identify where rules differ and specify which local procedure applies.
Do not promise that the policy itself solves every risk. A policy allocates responsibility and sets a repeatable process; controls, contracts, training, monitoring and competent decisions make it work. Where the organization cannot yet control a high-risk use, pause or narrow it until the missing control exists.
A publication-ready policy outline
Use this order when turning the decisions above into your document:
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- Scope and definitions: covered tools, systems, people, activities and key terms.
- Use categories: permitted, restricted and prohibited activities, with approval thresholds.
- Data and confidentiality: classifications, personal data, secrets, retention, output handling and approved environments.
- Human review: checks, decision authority, documentation and correction rights.
- Roles and governance: owners, reviewers, approvers, administrators and escalation chain.
- Tool and use approval: intake, assessment, pilot, conditions, register and reapproval triggers.
- Security and third parties: access, integration, monitoring, vendor assessment and change notices.
- Training and communication: role-based learning, examples, questions and acknowledgements.
- Incidents and exceptions: reporting, response, exception limits, expiry and enforcement.
- Monitoring and review: metrics, audit evidence, version control, cadence and change triggers.
- Related documents: links to privacy, security, data-governance, records, procurement and incident procedures.
Pre-publication quality check
- Can an employee identify whether a proposed use is allowed before entering data?
- Does every restricted use have a named reviewer and an approval record?
- Are confidential, personal, customer, regulated and secret data treated explicitly?
- Does a human with appropriate authority make consequential decisions?
- Are outputs checked for accuracy, bias, privacy, security and provenance?
- Do contractors and third parties know the same rules and reporting route?
- Can someone report an incident without placing more sensitive data into an unsafe system?
- Are monitoring, change management, review dates and incident response connected to existing procedures?
- Have legal, privacy, security and risk owners checked the draft for the relevant geography, sector and use cases?
- Will users be trained on the final wording and told where to ask questions?
Common drafting failures to avoid
A vague “use responsibly” rule
It does not tell staff which data or tasks are acceptable. Replace it with categories, examples, approval conditions and a reporting route.
A list of tools with no purpose or owner
Tools change and employees may use embedded features or personal accounts. Maintain an accountable owner, a defined purpose and a process for adding or removing systems.
One rule for every risk level
Overly strict rules block harmless work; overly loose rules expose consequential decisions. Use impact, data sensitivity and review capability to set different requirements.
Human review as a checkbox
A reviewer who lacks time, expertise, source information or authority cannot provide meaningful oversight. Specify the checks and the decision-maker.
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Ignoring the output
Generated text, code, images and summaries can carry errors, copied material, personal information or vulnerabilities. Apply governance to what the system produces, not only what someone submits.
Publishing once and forgetting it
New models, integrations, incidents and requirements can invalidate a rule. Set triggers, version the policy and communicate changes.
The strongest AI use policy is a living operating control: narrow enough to guide a real decision, broad enough to cover the tools people actually use, and connected to the owners and procedures that can act when something goes wrong.
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