AI Governance Checklist for Deploying Generative AI at Work

An effective AI governance checklist for deploying generative AI at work covers approved use cases, data handling, security, human oversight, testing, accountability, and ongoing monitoring. Use it before launch and revisit it as tools, risks, and workplace practices change; governance is a repeatable process, not a one-time approval.

Start with purpose, scope, and ownership

Write down what the system is intended to do, who will use it, and what decisions its output may influence. A tool that drafts internal meeting notes presents different risks from one that ranks job applicants or helps determine customer eligibility. Be specific about what the system must not do, too.

• Name a business owner responsible for the use case and a technical owner responsible for its configuration and operation.
• Involve the functions relevant to the risk, such as information security, privacy, legal, records management, procurement, accessibility, and human resources.
• Define the users, affected people, operating environment, and expected benefits. Identify a measurable way to evaluate whether the use case delivers them.
• Set approval and escalation routes, including who can pause or retire the system if problems appear.

Keep an inventory of generative AI tools and use cases, including tools staff have adopted without formal approval. Shadow use can expose confidential information or create inconsistent practices, so provide a clear route for employees to request review rather than relying only on prohibition.

Assess the tool and its data

Before choosing a service, understand how it handles prompts, uploaded files, generated content, and feedback. Review the provider’s documentation and contract terms for retention, access, deletion, training use, subprocessors, data location, and incident notification. Do not assume that a product’s general privacy statements answer every question about a particular account or configuration.

• Classify the information users may submit. Specify whether personal, confidential, regulated, customer, employee, source-code, or trade-secret data is permitted.
• Check whether prompts and outputs are stored, reviewed by people, or used to improve models, and what controls administrators can configure.
• Set access permissions, authentication requirements, retention periods, and deletion procedures appropriate to the use.
• Review integrations and connected data sources. Restrict access to the minimum needed for the task.
• Confirm how the organization can export its records or discontinue the service, and what happens to its data then.

Data minimization is a practical safeguard: users should provide only what a task requires. For example, an employee drafting a general policy summary usually does not need to paste a customer’s name, account number, or full case history into a prompt.

Set rules for outputs and human review

Generative systems can produce fluent but inaccurate or incomplete answers. Treat output as material to review, not as proof that a claim is true. Decide how much human scrutiny is required based on the consequences of an error, the complexity of the task, and whether the output affects an individual.

• Require a qualified person to verify factual claims, calculations, citations, and recommendations before consequential use.
• Do not let generated text make or finalize high-impact decisions without appropriate human judgment and established procedures.
• Tell users when and how they must disclose AI assistance, both internally and to external audiences where appropriate.
• Check generated content for confidential information, biased or exclusionary language, accessibility issues, and unsuitable tone.
• Keep a way for users or affected people to question, correct, or escalate an output when the workflow allows it.

Human review must be meaningful. A reviewer needs relevant expertise, sufficient time, access to supporting evidence, and authority to reject or correct the result. A person who simply approves every answer without checking it is not an effective control.

Test security, quality, and foreseeable misuse

Evaluate the exact workflow—not just the model in isolation—before deployment. Results can vary with prompts, data, user behavior, connected tools, and model updates. Test with representative examples, including difficult edge cases and inputs likely to cause errors.

• Measure task performance against a baseline and define what level of error is acceptable for the use case.
• Test for fabricated facts, poor handling of uncertainty, inconsistent responses, and performance differences across relevant groups or scenarios.
• Check security controls, including permissions and the treatment of untrusted text or files. Consider whether an input could manipulate the system or cause it to reveal information it can access.
• Verify that connected tools cannot take consequential actions beyond their intended permissions.
• Document test methods, results, known limitations, and unresolved risks; retest after significant changes.

A successful demonstration is not enough. A system that summarizes ordinary documents well may still fail on unusual formats, ambiguous instructions, or outdated source material. Where reliable answers depend on an authoritative source, establish how users check that source rather than trusting a confident-sounding response.

Train users and define acceptable use

Publish concise, accessible rules that explain approved tools, permitted tasks, prohibited data, review responsibilities, and incident reporting. Provide examples tailored to common jobs. Explain that generated material may be wrong, that users remain responsible for work they submit, and that sensitive data should not be entered unless the tool and use have been approved for it.

Training should be practical and recurring. Show how to verify claims, avoid unnecessary personal information, recognize limitations, and report a suspected disclosure or harmful output. Give employees a contact for questions and a route to request a new use case. Update guidance when system capabilities, policies, or observed risks change.

Monitor, document, and revise

Governance continues after launch. Assign someone to monitor whether the system is used as approved and whether it remains accurate and suitable. Define how users report errors, security concerns, privacy issues, biased outcomes, or unexpected behavior, and who investigates each type.

• Record the use case, owners, approved configuration, data rules, review controls, test evidence, and decision to deploy.
• Track relevant incidents, user feedback, output quality, and changes in the underlying service or connected data.
• Review access and permissions periodically; remove access when roles change or the use case ends.
• Set review triggers, such as a major model update, a new data source, a changed business purpose, or a pattern of errors.
• Maintain a rollback or shutdown plan, and communicate what users should do if the tool becomes unavailable or is withdrawn.

Keep documentation proportionate: a low-risk drafting assistant may need a lighter review than a system affecting employment or access to services. However, every use should have an owner, clear boundaries, and a way to raise concerns.

Frequently Asked Questions

What should an AI governance policy include?
A policy should define approved uses, prohibited or restricted data, review obligations, disclosure expectations, ownership, training, incident reporting, and how tools and use cases are approved and monitored.

Can employees use public generative AI tools at work?
That depends on the organization’s policy and the specific tool’s data practices. Employees should use only approved services for work information and should not submit sensitive material unless the organization has explicitly authorized that use.

Does a human need to check every AI-generated answer?
Review should match the risk. A low-stakes draft may need ordinary editing, while factual, customer-facing, or consequential material needs more careful verification by someone qualified to assess it.

How often should an organization review an AI use case?
Set a regular review schedule and also review when something material changes, such as the model, connected data, intended purpose, user group, or pattern of errors. The appropriate interval depends on risk and operating context.

What is the first step if an AI tool exposes sensitive information?
Follow the organization’s incident-response process promptly. Notify the designated security or privacy contact, preserve relevant details, and avoid further sharing while the responsible team assesses the exposure and required response.

Related

• California AI Hiring Rules: Employer Guide
• Peel the Potato Event Schedule & Guide
• Moto G37 Power 5G vs OnePlus N6X 5G: Key Differences