Company AI policy: practical rules for using artificial intelligence

Artificial intelligence is already part of everyday work. People use it to write, summarize, analyze data, develop software, and prepare presentations.

In many companies, however, actual use is growing faster than the rules. Some tools are approved, while others are opened through personal accounts. Documents and code fragments may be copied into external services without the user knowing how that information will be handled.

A company AI policy provides simple, practical guidance. It should not be a document written to ban everything, nor a generic list that nobody consults.

A good policy answers four questions.

Which tools may be used? Which data may be entered? Which activities require human review? What happens when a situation is not covered by the rules?

Why banning everything does not solve the problem

A complete ban may appear to be the safest option, but it often moves usage outside the company's view.

When a tool saves time and no approved alternative exists, people may continue using it through personal accounts. The organization then loses both control and the opportunity to train employees properly.

Complete freedom creates the opposite problem. Each person chooses providers, settings, and data without any common criteria.

The more realistic approach is to enable useful applications within understandable boundaries.

What a company AI policy should contain

The policy can begin with a small number of concrete elements.

Approved tools

State which services are authorized and which type of account should be used. Also explain how someone can request the evaluation of a new tool.

Data classification

Clarify which information must not be entered, such as personal data, trade secrets, credentials, or confidential code, unless approved environments and conditions are in place.

Permitted activities

Distinguish low-risk uses, such as generating a first draft, from activities that require additional controls, such as decisions involving people, customers, or legal matters.

Human review

Specify when AI-generated output must be checked before it is used or published.

Ownership and reporting

Explain who can answer questions, how an error should be reported, and what to do when AI has been used incorrectly.

Costs and limits

Define budgets, thresholds, or approval rules so that usage-based spending does not grow without control.

From a written rule to an applied rule

A common weakness of many policies is that they remain in a document separate from the work.

Someone may have read the rules months earlier and no longer remember how they apply to a specific situation. They may also be unaware that the service they are currently using has not been approved.

DevKira brings AI tools into an environment managed by the company. This connects the rules to the point where the tools are actually used.

Depending on the configuration, it is possible to:

• make only approved services available;
• apply common criteria to teams and projects;
• restrict or flag destinations that are not permitted;
• show contextual warnings before a risky action;
• record relevant events;
• connect usage to budgets and spending limits;
• separate experiments from production environments.

The policy therefore becomes part of the workflow, rather than an attachment people sign and forget.

A practical example

A company establishes that identifiable customer data may not be sent to unapproved generative AI services.

An employee tries to use a new assistant to summarize a support request. Inside the managed environment, a warning reminds them of the rule and points to the approved tool. The employee removes unnecessary personal data and continues the task using the authorized service.

The rule appears at the moment it is useful. The company does not need to wait for a later audit to discover the problem.

How to introduce the policy without creating resistance

A policy works better when it is developed with the people who will use it.

A practical process is to:

1. observe existing uses, including informal ones;
2. collect the cases where AI creates the most value;
3. identify the most sensitive data and activities;
4. define a small set of initial rules with concrete examples;
5. provide approved tools that are genuinely usable;
6. train people using real scenarios;
7. review the policy as services, regulations, and processes change.

The message should be clear: the objective is to enable responsible use, not to find someone to blame.

Frequently asked questions

Does the policy apply only to developers?

No. It should cover every role that uses AI tools, with guidance appropriate to different activities.

Who decides which tools are approved?

IT, security, privacy, procurement, and process owners are usually involved. The decision should consider usefulness, data handling, contractual terms, costs, and integrations.

What happens when someone breaks a rule?

That depends on the seriousness of the event and company policy. A system may show a warning, block an action, or create an alert. The organizational response should be proportionate and transparent.

Will employees feel monitored?

That risk exists when the purpose and methods are not explained. The company should clearly state which events are recorded, why they are recorded, and who can review them.

Should the policy mention the EU AI Act?

The AI Act and other applicable rules may need to be considered, but the content depends on the company's use cases. Legal and compliance specialists should be involved where appropriate.

The next step

An effective AI policy should connect to AI cost control, access traceability, and secure spaces for testing new tools.

See how to apply AI policies inside DevKira.