Generative AI Policy: A Prescriptive Guide for Businesses

A useful policy does not try to ban all AI or turn every query into a bureaucratic process. Its purpose is to reduce ambiguity: allow low-risk work, put controls in place where the impact is greater, and document who decided what.

The starting point should not be a list of brands, either. The same platform may have different terms depending on the plan purchased, configuration, region, type of integration, and data processing agreement. That is why the policy should govern data, uses, and responsibilities, in addition to naming approved tools.

# What problem does an AI use policy solve?

When an organization adopts AI without shared rules, each person decides for themselves what information to paste into an assistant, whether a response can be sent to a client, or whether generated code can go into production. The risk lies not only in the tool, but also in invisible decisions that are difficult to reconstruct.

A policy turns those decisions into an operating system. It defines limits, approval paths, and minimum evidence. It should connect to existing controls—information security, privacy, procurement, software development, records management, and incident response—instead of creating a parallel universe.

The NIST AI Risk Management Framework treats governance as a cross-cutting function: it calls for transparent policies, documented responsibilities, inventories, monitoring, and third-party controls. Its generative AI profile adds a focus on pre-deployment testing, content provenance, documentation, and incident disclosure.

# The 10 components it should include

# 1. Scope and definitions

The policy should specify whom it applies to: employees, executives, contractors, suppliers, and any third party with access to company information or systems. It should also cover text assistants, image generators, code copilots, AI features built into software, and agents that take actions.

It is helpful to distinguish among three activities because they do not carry the same risks:

• using a tool for individual support;
• integrating a model into a process through an API;
• allowing an agent to access systems or take actions.

# 2. Approved tools, accounts, and configurations

Approval should identify the product, plan, internal owner, authorized purpose, and minimum required configuration. “Use ChatGPT” or “use Claude” is not enough to determine whether a personal account, business account, or integration is permitted.

Before approving a tool, the organization should review at least the following: current terms, data processing and retention, administrative controls, identity and access, available logs, relevant data locations or transfers, subprocessors, opt-out options, and deletion procedures.

# 3. Information classification

The policy should build on the classification system the company already uses. If there isn't one, it can start with a simple version and assign an owner to formalize it.

The table does not replace a privacy or security analysis. Its value is to offer a clear initial decision for employees.

# 4. Permitted, conditional, and prohibited uses

An effective policy avoids the “allowed/prohibited” binary. The conditional category recognizes that some uses are valid only with review, anonymization, a contract, or a controlled environment.

Examples should be adapted for each department. A marketing draft and a clinical recommendation do not require the same level of control.

# 5. Human review and external approval

The phrase “review the output” is not enough. The policy should clarify who reviews it, what they check, and what evidence they retain.

The review may include accuracy, sources, confidentiality, bias, tone, intellectual property, code security, or sector-specific compliance. For external content, the organization should maintain the same approval process it would use if the material had been written without AI.

No one should automatically sign off on an output they cannot evaluate. For medical, legal, financial, employment, credit, or safety matters, reviewers need sufficient expertise and authority.

# 6. Personal data, privacy, and confidentiality

In Mexico, the current LFPDPPP is a new law published on 20 March 2025 and last amended on 14 November 2025. The law designates the Secretariat of Anti-Corruption and Good Governance as the authority and requires the principles of lawfulness, purpose, loyalty, consent, quality, proportionality, information, and accountability.

The law also requires administrative, technical, and physical safeguards commensurate with the risk, sensitivity, and state of technological development. Therefore, using an AI platform with personal data is not addressed by a blanket prohibition, nor is it authorized simply by purchasing an enterprise plan. You need to review the purposes, privacy notice, applicable legal basis, relationship with the provider, transfers, security, retention, and individuals’ rights.

This article provides general operational guidance, not legal advice. The final policy should be reviewed by Mexican specialists in privacy, labor, intellectual property, and sector-specific regulation, as applicable.

# 7. Software development and automation

For AI-generated code, the policy should require version control, technical review, testing, security analysis, and verification of licenses or dependencies. Never allow credentials, secrets, keys, real data, or restricted code to be copied into an unapproved tool.

Agents and automations need additional controls: least-privilege permissions, test environments, spending or volume limits, action logs, a stop mechanism, and an operations owner. A text suggestion and an irreversible action do not belong in the same risk category.

# 8. Vendors, procurement, and integrations

The assessment should cover both the primary vendor and relevant integrators, add-ons, and subprocessors. The contract and configuration should match actual use.

Minimum questions:

• What data does the provider receive, and what does it use it for?;
• How are retention and deletion controlled?;
• What administrative access and logs are available?;
• How are incidents and material changes reported?;
• What happens to the data and workflows when the service ends?;
• What restrictions apply to training, evaluation, or product improvement?;

It is not advisable to copy commercial promises from a public webpage into the policy. The current terms of the applicable contract should be documented in the assessment file.

# 9. Incidents, exceptions, and records

The policy should explain where to report a data exposure, harmful output, unauthorized integration, or accidental use of a personal account. An early report should trigger containment, not an immediate search for someone to blame.

Exceptions need a duration, scope, owner, compensating controls, and a closing date. It is also helpful to maintain a record of tools, use cases, owners, decisions, incidents, and configuration changes.

# 10. Training, compliance, and review

Signing a document does not demonstrate that someone knows how to apply it. Training should use real work scenarios: summarizing a contract, caring for a patient, analyzing a call, generating code, or publishing content.

Review frequency should not be copied from a template. Reviews can be triggered by vendor changes, new integrations, incidents, regulatory changes, entry into a regulated sector, or an increase in the level of autonomy. ISO/IEC 42001 can serve as a reference for structuring a management system and continuous improvement; adopting the standard or seeking certification is a separate decision.

# How to implement the policy without slowing operations

# Step 1: discover actual usage

Interview representative teams and record tools, accounts, data, tasks, and outputs. Don’t start only with the software Procurement knows about: AI is often built into products already in use.

# Step 2: prioritize by impact and exposure

Classify each use case by information type, people affected, ability to correct the output, and system autonomy. A frequent but reversible use may require different controls from an infrequent use that affects rights or money.

# Step 3: approve an initial set

Define tools and low-risk use cases that can be used from day one. Pair them with clear examples of what must not be entered and a simple path for requesting an exception.

# Step 4: train with business scenarios

Training should show how to make decisions, not just how to write prompts. Include exercises on identifying data, verifying outputs, escalating questions, and reporting incidents.

# Step 5: measure and adjust

Review adoption, exceptions, incidents, authorization times, discontinued uses, and vendor changes. A policy that is not updated to reflect operations becomes merely decorative.

# Quick decision template before using AI

Before starting a task, users should be able to answer:

1. Is the tool, account, and purpose approved?
2. What information am I going to share, and how is it classified?
3. Does the use comply with the applicable notice, contract, and obligations?
4. Who could be affected by the result?
5. Who will review the output, and by what criteria?
6. What record or evidence should I keep?
7. How do I stop or report the process if something goes wrong?

If an answer is uncertain, the policy should indicate whom to contact. Clarity about that path is one measure of the document’s quality.

# Frequently asked questions

# Can one AI policy work for every company?

No. The structure can be reused, but data, systems, contracts, risks, and regulations vary. A clinic, a law firm, and an industrial company need different controls, even if they share principles.

# Does purchasing a business account authorize any use?

No. A license may offer administrative controls and different terms, but it does not replace data classification, provider assessment, a legal basis, configuration, or human review.

# Should personal accounts be prohibited?

For company information or processes, the organization should define which accounts and environments are authorized. A prohibition alone does not solve the problem: useful alternatives, training, and access controls are also needed.

# Who should be responsible for the policy?

There should be an operational owner with the authority to maintain it, but important decisions require a multidisciplinary approach. Business, technology, security, privacy, legal, and affected teams should participate as appropriate.

# How often should it be updated?

When risks or conditions change: new tools, integrations, incidents, terms, data, processes, or regulations. The organization should also set a schedule for periodic reviews.

# From policy to controlled use

The deliverable is not the signed PDF. It is a system where people know what they can do, relevant use cases have an owner, and exceptions are documented.

If your company needs to turn scattered AI use into actionable rules, ia¹ can help build an inventory, classify risks, assess tools, and design an adoption pilot with owners and metrics. Request a feasibility assessment.

# Primary sources

• Chamber of Deputies — LFPDPPP, current text
• NIST — AI Risk Management Framework Core
• NIST — Generative AI Profile
• ISO — ISO/IEC 42001:2023