AI, explained
Do I need an AI policy for my company?
If anyone on your team has pasted work into an AI tool, the question is already answered: you have AI in the business, the only choice is whether it is governed.
Most companies do not decide to adopt AI, they discover they already have. Someone in marketing drafts copy in a chatbot, someone in finance summarises a contract, someone in support pastes a customer email to get a reply written. This is happening now, and without a policy it is happening with no rules about what data is safe to share or whether the output can be trusted. A policy does not slow that down, it makes it safe.
The real risks a policy addresses
- Data leakage. Confidential or personal data pasted into a consumer tool may be stored or used for training. Under GDPR that can be a reportable problem.
- Quality and accountability. AI output can be wrong or fabricated. Someone has to own the check before it reaches a client or a decision.
- Inconsistency. Ten people using ten different tools in ten different ways produces work that does not look like it came from one company.
- Legal and IP exposure. Questions of copyright, client confidentiality, and regulated advice all change when a machine drafts the first version.
What a workable policy actually says
A useful AI policy is short and specific, not a legal treatise nobody reads. It names the approved tools and their approved uses. It draws a hard line around data: what categories (client PII, financials, credentials, unreleased material) must never be entered into any external AI tool. It requires human review before AI-assisted work is sent externally or used in a decision. It clarifies that a person, not the tool, remains accountable for the result. And it points to who to ask when a new use case comes up.
Why "we will wait and see" is the risky option
Waiting does not pause AI use, it just leaves it ungoverned. The companies that struggle later are the ones that let shadow usage grow for a year and then try to claw it back. A one-page policy issued early, then refined as real use cases appear, costs almost nothing and removes most of the downside. It also unlocks the upside, because staff who know the rules use the tools more, not less.
If you want the policy grounded in how AI would actually be used across your specific workflows, rather than a generic template, that is exactly what an AI audit produces alongside the risk map. Digiton works to EU (GDPR) and Portuguese requirements as standard.
Frequently asked questions
Do I need an AI policy for my company?
Yes, if your staff use AI tools at all, and most do already. Without a policy, employees make individual decisions about what data to paste into external tools and whether to trust the output, which creates data-protection, quality, and accountability risk. A short, clear policy naming approved tools, forbidden data, and human sign-off removes most of that exposure.
What should a company AI policy include?
At minimum: the list of approved tools and their allowed uses, a hard rule on what data must never be entered into external AI, a requirement for human review before AI-assisted work goes out, a statement that a named person stays accountable for the result, and a contact for new use cases. Keep it to a page so people actually read it.
Is using AI tools a GDPR risk?
It can be. Pasting personal or confidential data into a consumer AI tool may mean that data is stored or processed outside your control, which can breach GDPR. The mitigation is a policy that forbids entering personal and confidential categories into non-approved tools, plus using services with appropriate data-processing terms for anything involving real customer data.
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