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GOVERNANCE · SECURITY · RESPONSIBLE AI

AI governance: adopt artificial intelligence without losing control

Clear policies, per-tool permissions, an audit of every action and human approval where it matters. AI works for your company — under your rules.

Design your internal AI policy How it works
Permissions per toolA log of every actionA human on critical decisions
Quick answer

What is AI governance?

AI governance is the set of policies, permissions, controls and auditing processes that regulate how a company uses artificial intelligence: which tools are approved, what data they can touch, what actions an agent can take without human approval and how everything is logged for review. Without governance, AI gets in anyway — but through the back door: personal accounts, sensitive data in unapproved tools and decisions with no traceability. It is the natural complement to executive AI training AI Political Marketing AI Sports Marketing and the secure foundation of any agent-based automation project.

The problem

AI is already inside your company. Under what rules?

Your team is already pasting customer data into personal ChatGPT — with no one knowing.
There's no list of approved AI tools or criteria to evaluate them.
An automated agent could take actions with no limits and no record.
No one could answer a customer or auditor asking "what does your AI do with my data?"
Teams buy AI tools on their own, duplicating spend and risk.
There's no protocol if the AI makes a mistake with real impact.
What's included

What the service includes

📜

Internal AI usage policy

Clear, practical rules: what's allowed, what isn't, with which data and in which tools.

🗝️

Permissions model

What each agent or tool can read, write and execute — with least privilege.

🧾

Auditing and logs

A log of agent actions and tool usage, ready for internal or external review.

🙋

Human-in-the-loop

Designing the points where a person approves before execution: payments, shipments, sensitive decisions.

🔒

Data protection

Data classification, anonymization where it applies and tool selection based on how they handle data.

🧭

Framework + training

A process to evaluate new tools and training the team on the policy.

Use cases

Use cases

CORPORATEUsage policy for employees

Practical guidelines per team and a catalog of approved tools, without slowing productivity.

AGENTSAutomation with control

Agents with defined permissions, spending limits and human approval on critical actions.

REGULATEDHealthcare and finance

Handling of sensitive data, a record of decisions and evidence for audits.

VENDORSTool evaluation

A checklist to decide whether an AI tool gets in or not, based on data, cost and risk.

Process

How we work: 5 steps

Diagnosis

We map the real use of AI in your company (including what no one reports).

Solution design

We classify data and risks, and define the policy and the permissions model.

Implementation

We implement controls: access, logs, approvals and approved tools.

Testing

We test the critical flows and simulate incidents.

Measurement and optimization

We review quarterly: new tools, new risks, adjustments.

Integrations

Tools we work with

Claude / OpenAI (enterprise plans) MCP with permissions n8n self-hosted SSO / access management Audit logs and logging DLP and data classification
Benefits

Concrete benefits

Faster AI adoption because there are clear rules, not fear.
Customer data protected: nothing sensitive in unapproved tools.
Every agent action is traceable and reversible.
A solid answer for customers and auditors about your AI use.
AI tool spend consolidated and justified.
Errors contained: limits and approvals prevent the big damage.
FAQ

Frequently asked questions

What leadership, legal and IT ask about the safe use of AI.

Design your internal AI policy
01Why do I need governance if we're barely getting started with AI?+

Because your team already uses it — internal surveys usually reveal that more than half use personal AI for work. Starting with clear rules costs little; cleaning up the chaos after an incident costs a lot.

02Doesn't governance slow down AI adoption?+

The opposite: the main blocker is fear ("can I use this?", "will I get in trouble?"). A clear policy with approved tools frees people to use AI with confidence.

03What is human-in-the-loop?+

It means designing the exact points where a person reviews or approves before the AI acts: a payment, a mass email, a decision about a customer. The AI prepares; the human authorizes wherever a mistake would be costly.

04How do you protect the data that AI agents touch?+

With data classification (what is sensitive and what isn't), least-privilege permissions per tool via MCP or APIs, anonymization where it applies and selecting providers with clear commitments not to train on your data.

05Does this only apply to large companies?+

No. A 20-person SMB using ChatGPT without rules has the same kind of risk, at its scale. A policy for an SMB fits in a few pages and is implemented in weeks.

The question isn't whether your company will use AI. It's under what rules.

Book the diagnosis: we map your current AI use and deliver the policy and the control plan.

Design your internal AI policy hola@domotics.mx
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