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Build the directory as a governed inventory of tools plus specific use cases, not as a list of app names. Each entry should state who may use the tool, the exact task allowed, permitted and prohibited data, required human checks, evidence from security and privacy reviews, an accountable owner, and the next review date. Keep separate statuses for discovered, under review, approved with conditions, restricted, suspended, and retired. This makes the directory useful to employees while giving the organization an auditable way to control changing models, vendors, integrations, and regulations.
What is an approved AI tool directory?
An approved AI tool directory is an employee-facing view of an internal AI inventory. It answers two questions separately: what technology exists and what people are allowed to do with it. A product can be visible in the directory for discovery or assessment without being approved for every department, data type, or decision.
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NIST describes an AI system inventory as “an organized database of artifacts relating to an AI system or model.” Those artifacts can include system documentation, incident-response plans, data dictionaries, implementation links, and contact details for relevant AI actors. The inventory is a governance record; the directory is the readable, searchable presentation of the subset employees need.
Use one record for each tool-and-use-case combination when the same product has materially different risks. For example, drafting a public blog outline from public information may be low risk, while sending customer records to the same service is a separate, potentially high-risk use case. A single green “approved” badge without that distinction invites unsafe interpretation.
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How should you define the inventory’s scope?
Start by writing a scope statement that says what counts as an AI system, which business units and geographies are covered, and which embedded features are included. Include standalone applications, APIs, plug-ins, browser extensions, office-suite features, model-enabled devices, and vendor services that make decisions or generate content on the company’s behalf.
Capture discovered tools before they are approved
Discovery is not permission. Accept reports from procurement, IT, security telemetry, expense records, business teams, and employees. Create a record as soon as a tool is identified, then mark it discovered until an owner and review path are assigned. This prevents an unlisted service from being mistaken for an approved one while the organization is still learning where AI is being used.
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- Discovered: the organization knows the tool or feature exists, but no review decision has been made.
- Under review: evidence is being collected and reviewers are assessing a defined use case.
- Approved with conditions: the use case is allowed only within the stated users, data classes, controls, and dates.
- Restricted: use is limited to named teams, environments, or test data.
- Suspended: access or use is paused because a control failed, an incident occurred, or the risk changed.
- Retired: the organization no longer permits the use case and has completed offboarding and record retention steps.
Prefer complete coverage, then prioritize risk
NIST recommends complete inventories where feasible, or at minimum coverage of high-risk systems and systems used in high-stakes settings. If a full census is not immediately possible, document the gap, prioritize systems handling sensitive information or making consequential recommendations, and expand coverage through a defined intake process rather than treating the pilot list as complete.
Who should own and approve directory entries?
Give every entry a named business owner and a separate accountable approver. The business owner explains the task and benefit, ensures users follow the conditions, and confirms that the use case remains necessary. The accountable approver has authority to accept residual risk, impose conditions, deny the request, suspend the entry, or retire it.
A practical governance pattern adapts the roles described by the U.S. General Services Administration: a senior AI leader coordinates the program, a cross-functional governance board sets direction, and an oversight group handles intake, risk posture, and rights and safety issues. Smaller organizations can combine these bodies, but they should not leave decision rights implicit.
Recommended participants
- Directory owner: maintains the system of record, status labels, employee view, and review calendar.
- Business owner: defines the task, users, expected benefit, quality threshold, and human accountability.
- Technical or service owner: documents architecture, integrations, identity controls, logging, and rollback options.
- Security: evaluates authentication, data flows, vulnerabilities, access, logging, and incident response.
- Privacy: evaluates personal-data processing, purpose limitation, retention, deletion, and data-subprocessor terms.
- Legal and compliance: checks sector rules, contractual restrictions, intellectual-property concerns, and jurisdictional requirements.
- Procurement: retains contracts, service-level commitments, transparency evidence, and renewal or termination terms.
- Records management: determines whether prompts, outputs, or decisions are business records and how they are retained.
- Accessibility: checks that employees with disabilities can use the workflow and that generated content receives appropriate accessibility review.
- IT and identity: provisions groups, single sign-on, lifecycle changes, and technical enforcement of restrictions.
Record who can approve, conditionally approve, deny, suspend, and retire an entry. Do not rely on a committee name alone; an auditor or employee should be able to identify the person or role accountable for each decision.
What information belongs in an AI approval record?
Use a structured record with required fields rather than a free-text approval email. The following set combines inventory attributes, operational controls, and third-party due-diligence evidence.
| Record area | Fields to capture | Why employees and reviewers need it |
|---|---|---|
| Identity | Tool and vendor name; product URL; version, model, or embedded feature; deployment environment | Prevents confusion between similarly named products, model versions, and test or production instances. |
| Use case | Specific employee task, expected benefit, output recipient, and prohibited purposes | Defines what the approval actually covers instead of implying blanket permission. |
| Accountability | Business owner, technical owner, directory owner, accountable approver, incident contact | Provides a person to answer questions and act when conditions fail. |
| People and geography | Permitted departments, user population, countries or regions, employment or customer groups affected | Supports jurisdiction-specific controls and prevents accidental expansion. |
| Data rules | Allowed, prohibited, and conditionally allowed data classes; masking or de-identification requirements | Makes the paste-or-don’t-paste decision visible at the moment of use. |
| Vendor terms | Retention, training use, deletion, subprocessors, data residency, export, and change-notice terms | Shows how submitted information is handled and what happens when terms change. |
| Assurance evidence | Security, privacy, legal, accessibility, records, procurement, and testing evidence; contract and service-level commitments | Lets reviewers verify that the decision rests on documented checks rather than an informal claim. |
| Decision | Risk tier, status, conditions, effective date, expiration or next review date, and change history | Shows whether the approval is current and what boundaries apply. |
| Operations | Monitoring measures, quality threshold, rollback or suspension trigger, incident process, and retirement plan | Turns the approval into a control that can be operated after launch. |
Keep source documents and reviewer comments attached to the record, with version history and access controls. The employee-facing page can omit sensitive contract details while preserving an internal audit trail and the evidence location.
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How do you classify AI use cases by risk?
A small number of understandable tiers is easier to operate than a complex score that employees cannot interpret. Use the tier to determine reviewers, controls, testing, and approval authority; then state the concrete conditions in plain language.
| Tier | Typical characteristics | Minimum gate |
|---|---|---|
| Low | Personal productivity, public or non-sensitive inputs, no autonomous action, and human review before sharing | Business-owner confirmation, basic security check, acceptable-use acknowledgement, and a clear prohibition on confidential or regulated data. |
| Moderate | Internal business data, persistent accounts, integrations, or outputs that influence material productivity decisions | Security and privacy review, business-owner approval, documented data-flow and retention controls, access restriction, and output-quality checks. |
| High | Regulated or sensitive data, employment or eligibility decisions, customer-facing outputs, safety-critical work, or autonomous actions | Formal legal, privacy, security, accessibility, records, and accountable-executive approval; documented testing, monitoring, human oversight, and a tested rollback or suspension plan. |
This risk-prioritized approach follows NIST inventory guidance, the GSA governance model, and IRS policy that AI is governed by use case and that operational use begins only after approval by an accountable official. A tool’s brand, popularity, or technical sophistication does not determine its tier; the task, data, users, and consequences do.
How do you write an approval that employees can follow?
Put the boundary next to the approval badge. Indiana’s state guidance illustrates why: only tools with an accompanying AI-policy exception are allowable for state employees, and inclusion in an inventory does not mean a tool is approved for every agency or use case. The same principle applies in a company directory.
Use a one-sentence scope statement
Write the approved action, audience, and data boundary in one sentence, such as: Approved for drafting internal, non-confidential meeting summaries for the operations team; do not enter personal data, regulated data, customer secrets, or source code. Follow it with separate “Allowed,” “Do not enter,” and “Before sharing” lists.
State the human check
Tell users exactly what they must verify: factual accuracy, calculations, citations, confidentiality, bias, accessibility, tone, or compliance with a customer or employment obligation. Require a named employee to remain accountable for publishing, sending, or acting on the output; the tool does not become the decision-maker merely because it generated the text.
Repeat the warning in the workflow
Show prohibited-data rules on the listing, at first access, and in the request or launch flow. IRS guidance warns that entering work information into public or non-agency AI tools can create disclosure risks and places responsibility on employees for their prompts and for using, publishing, or distributing outputs. A short acknowledgement should link to the applicable policy and record the employee’s acceptance before access is granted.
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What should an employee see on one directory page?
Design for a quick decision, not a catalog of vendor marketing copy. Every listing should answer these questions without opening a policy PDF:
- What task is approved? Use a verb and an outcome, such as “summarize internal project notes for team review.”
- Who may use it? Name departments, roles, geography, account type, and any pilot limitation.
- What data may be entered? Use the organization’s data-class labels and give a concrete example.
- What is forbidden? Put the prohibition beside the status badge, including personal, regulated, customer, source-code, or secret information where applicable.
- What check is required? State the human review and whether a second person must approve an external or consequential output.
- How do I report a problem or request a new use case? Provide one intake link or service-desk route and an incident contact for urgent concerns.
- When is the entry reviewed? Show the effective date, next review date, and a warning if the review is overdue.
Offer search filters for task, department, data class, geography, status, and risk tier. Keep internal evidence and contractual details behind role-based access while exposing enough information for an employee to make a safe choice.
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How can employees request a new AI tool or use case?
Use a consistent intake workflow so an urgent business request does not bypass review. The form should ask for a use case, not merely a product name.
- Describe the task and benefit. Identify the current process, users, expected output, affected people, and why existing approved tools are insufficient.
- Describe the data flow. List input data classes, output destinations, integrations, geography, account identities, retention needs, and whether prompts or outputs are used for training.
- Name owners and boundaries. Provide a business owner, technical contact, proposed users, prohibited purposes, and the employee accountable for final decisions.
- Assign a provisional risk tier. Flag regulated data, employment or eligibility decisions, customer-facing content, safety impact, autonomous actions, or persistent integrations for high-risk review.
- Collect vendor evidence. Obtain security and privacy documentation, deletion and retention terms, subprocessor information, residency and export terms, change-notice commitments, accessibility information, and service-level agreements where relevant.
- Run the review gates. Route the request to security, privacy, legal, procurement, records, accessibility, and the accountable business approver according to its tier.
- Test before broad access. Use representative but non-sensitive data, define quality and failure thresholds, test prompt and output handling, and document unresolved limitations.
- Publish a conditional entry. Set the status, users, data rules, human checks, effective date, next review date, incident route, and suspension trigger in the directory.
- Provision access last. Grant the approved group or environment only after the employee accepts the relevant policy and acknowledgement.
Reject or return incomplete requests with a specific missing-evidence list. That creates a predictable path for resubmission instead of encouraging shadow use.
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The directory is informative unless technical controls support it. Connect approved entries to identity groups, single sign-on, application catalogs, network controls, data-loss-prevention rules, or API gateways where those controls are available.
- Create groups that match the approved audience, such as a department, role, geography, or pilot cohort.
- Use automated joiner, mover, and leaver processes so a transfer or departure removes access promptly.
- Separate test and production environments, and prevent production data from reaching a test use case.
- Log access grants, policy acknowledgements, administrative changes, and high-risk use where lawful and proportionate.
- Block or warn on prohibited data classes when technical detection is reliable, while keeping the employee rule authoritative.
- Reconcile the directory with procurement and identity records to find accounts that exist outside the approved population.
Do not claim that a network block proves an action is permitted. An employee may reach a service through a personal account or an unapproved integration; the policy and directory still define the organization’s rule, while technical controls reduce the opportunity for accidental or deliberate bypass.
When should an approval be reviewed after it is published?
Make the directory a living control. IRS policy requires inventory maintenance when a change meaningfully affects accuracy and at least annual validation. Add event-driven reviews so the annual date is a floor, not the only trigger.
Trigger an immediate review for
- A new model, major version, feature, plug-in, API, or autonomous capability.
- A change to the vendor’s privacy policy, training use, retention, deletion, subprocessor list, residency, or export terms.
- A new integration, user population, geography, data class, or business purpose.
- A security incident, suspected disclosure, legal complaint, accessibility problem, or material output-quality failure.
- A change in law, regulation, contract, internal policy, or risk appetite.
- An overdue owner response, failed control, or unexplained increase in exceptions or usage.
Monitor useful operating signals
Track adoption, active users, exception requests, incidents, output-quality complaints, failed acknowledgements, access outside the approved group, and entries approaching their review date. Monitoring should lead to an action: reaffirm, add a condition, narrow the user group, require new testing, suspend, or retire.
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Retain an auditable history
Keep prior versions of the record, decisions, evidence, conditions, approvals, incidents, and suspension or retirement actions. The Federal Reserve’s public AI Use Case Inventory demonstrates annual publication and maintenance of use-case records under federal direction. A private employer does not need to publish sensitive details, but should retain an internal history and provide employees with a sanitized current view.
What should happen when a tool or model changes?
Do not silently carry forward an approval after a material change. Mark the entry as under review, notify affected users, and determine whether the change alters data handling, output behavior, risk tier, or contractual protection.
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- Capture the vendor notice, release identifier, effective date, and affected entry.
- Compare the new data flows, retention and training terms, subprocessors, security controls, and capabilities with the approved baseline.
- Ask the business owner to confirm that the task, users, expected benefit, and quality threshold remain valid.
- Run targeted regression, privacy, security, accessibility, and output-quality tests using safe test data.
- Decide whether to reaffirm, add conditions, narrow access, require re-acknowledgement, or suspend use.
- Update the change history and publish the employee-facing notice before or when the change takes effect.
If the vendor cannot provide enough information to reassess a material change, treat that uncertainty as a reason to restrict or pause the affected use case rather than assuming the old decision still applies.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you respond to an incident or unsafe output?
Publish one reporting route and define a fast suspension path. Employees should be able to report exposed data, fabricated or harmful output, discriminatory behavior, inaccessible content, suspicious account activity, policy violations, or a vendor security notice without deciding which committee owns the problem.
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- Contain: stop the affected workflow, revoke or narrow access if necessary, preserve relevant prompts, outputs, logs, and vendor notices, and prevent further disclosure.
- Triage: classify the issue as security, privacy, legal, safety, accessibility, records, quality, or a combination; notify the incident contact and accountable owner.
- Assess impact: identify people, data, jurisdictions, customers, decisions, and downstream systems affected.
- Decide on status: keep, restrict, suspend, or retire the entry, documenting who made the decision and why.
- Remediate: correct data handling, prompts, permissions, training, contracts, or human review; notify affected parties when required.
- Reauthorize: test the fix, update conditions and evidence, and require renewed acknowledgement before restoring access.
IRS policy specifically describes suspending use, securing data, and remediating non-compliant high-impact AI. Include those actions in the directory’s operating procedure instead of treating the listing as a static web page.
Should you use a spreadsheet, service-management tool, or GRC platform?
Choose the implementation that can preserve use-case granularity, evidence, decision rights, and lifecycle history. A technically sophisticated platform is not automatically better if reviewers and employees cannot keep it current.
| Implementation | Works well when | Limitations to address |
|---|---|---|
| Spreadsheet plus document repository | A small pilot has few tools, reviewers, and jurisdictions; the team needs to start quickly. | Version control, access management, reminders, duplicate records, workflow, and audit exports require disciplined manual practices. |
| Service-management catalog | Employees already use a service desk for requests, approvals, incidents, and identity-linked access. | Risk scoring, vendor-change evidence, model versions, and complex data-flow documentation may need extensions or linked records. |
| GRC or AI-governance platform | Many tools, reviewers, jurisdictions, evidence types, recurring reviews, and audit requirements justify automation. | Implementation effort, licensing, configuration, and user training can become a control burden if the process is not defined first. |
Compare options on use-case granularity; identity and access integration; security, privacy, legal, procurement, records, and accessibility workflow; evidence retention; risk scoring; vendor-change monitoring; employee search and readability; audit exports; incident and suspension controls; and total operating effort.
What are the most common directory failures?
A list of products is mistaken for approval
Fix: Put the approved task, audience, data boundary, human check, and review date in the same record as the product name.
One approval covers every department
Fix: Split records when purpose, data, users, geography, or consequences differ. A marketing drafting use case should not inherit permission to process employee or customer records.
Policies are detached from the employee decision
Fix: Repeat the prohibited-data rule and human-review requirement on the listing and in the acknowledgement flow, with a direct route for questions and incidents.
Evidence is collected once and then forgotten
Fix: Assign an owner, next review date, event triggers, version history, and a suspension path. Require vendor-change notices and annual validation.
Access is granted before approval
Fix: Make identity provisioning depend on the approved group, current status, and policy acknowledgement. Reconcile actual accounts with the directory.
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What does a complete directory entry look like?
A concise employee view might read:
- Tool and feature: named product, model or embedded feature, and production environment.
- Status: approved with conditions; effective date and next review date.
- Approved task: draft internal meeting summaries for the operations team.
- Users: operations employees in the specified region with managed accounts.
- Allowed data: public information and internal, non-confidential notes.
- Forbidden data: personal data, regulated records, customer secrets, source code, credentials, and security incidents.
- Required check: an employee verifies names, dates, decisions, action owners, confidentiality, and accessibility before distribution.
- Owner and contact: named business owner, incident route, and request path for a different use case.
- Controls: managed identity, logging, retention and deletion terms, vendor evidence, and suspension trigger.
The internal record would additionally hold contracts, reviewer comments, test results, data-flow diagrams, subprocessor information, risk rationale, and the complete change history.
How should you launch the directory?
Launch in controlled stages while keeping the governance model consistent.
- Set the rule: publish the definition of an AI system, lifecycle statuses, data classes, risk tiers, approval authority, and incident route.
- Build the initial inventory: gather known tools and embedded features from procurement, IT, security, and business teams; label unknowns as discovered.
- Prioritize: review high-risk and high-stakes uses first, then expand to moderate and low-risk productivity cases.
- Create the employee view: provide searchable listings with task scope, users, data rules, human checks, contacts, and review dates.
- Connect controls: link approved statuses to identity groups, acknowledgements, service-desk intake, monitoring, and incident handling.
- Measure and refine: inspect exceptions, complaints, overdue reviews, and unlisted usage; simplify fields that reviewers cannot maintain and strengthen controls where incidents show gaps.
The result should be a living decision system: employees can find a safe, approved path for a task, while the organization can show who authorized it, under what conditions, on what evidence, and when the decision must be reconsidered.
Frequently Asked Questions
Can employees use an AI tool that is not listed in the directory?
Treat an unlisted tool as undiscovered, not implicitly approved. Employees should submit the task and data flow through the intake process, and managers should avoid granting exceptions through informal email or chat.
How should contractors and temporary staff be handled?
List them explicitly in the permitted-user field and apply the same acknowledgement, identity, data, and supervision controls as employees. If their accounts, geography, or contractual duties differ, create a separate use-case record rather than inheriting the employee entry.
Should prompts and outputs be saved for every approved use?
Not necessarily; retention depends on the task, legal obligations, records rules, privacy impact, and incident needs. Define in the record which prompts or outputs are business records, what evidence must be preserved for high-risk decisions, and when unnecessary content should be deleted.
How can accessibility be checked for AI-generated documents?
Test the complete workflow, including the directory, sign-in, generated output, editing tools, and publication channel with the assistive technologies used by your workforce. Check structure, headings, tables, captions, color contrast, reading order, and keyboard operation rather than assuming an accessible interface produces accessible content.
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