Copywriting, email drafting, document summarization, client communication, HR screening — companies are deploying AI for an ever-growing range of everyday office tasks. This makes it increasingly important that all of this happens within a proper framework: transparently, lawfully, and securely.
AI can deliver real business value only when these elements are in order — thereby, we eliminate the ever-present legal risks. And those legal risks are growing alongside increasingly stringent EU data protection and AI regulations.
In AI projects where we assist clients from a legal perspective, we find that successful AI adoption rests on four pillars:
- A well-considered AI Audit,
- Selecting the right AI tools,
- A clear AI Use Policy, and
- Targeted training and upskilling for employees.
Step 1: AI Audit — Mapping Your Organization’s AI Usage
The first question is: where and how is your organization already using artificial intelligence? In our experience, most companies have “shadow AI”: employees independently, without approval, start using ChatGPT or other tools — sometimes feeding in client data and confidential information.
The goal of an AI Audit is to bring order to this chaos and answer three key questions: Is AI use actually necessary in a given process? Does current practice comply with applicable law (particularly the EU AI Act and GDPR)? And what risks is the company taking on with its current operations? To answer these, you first need to define what counts as “AI” within the organization (e.g., chatbots, generative tools, CV screening systems) and what is simply conventional software in the classic sense.
In practice, the AI Audit involves mapping all AI systems and use cases, with input from all key departments (HR, IT, Legal, Marketing, Sales). Vendor contracts and existing data processing records must be reviewed. This is followed by a risk assessment and legal classification: which systems fall into higher and which into lower risk categories under the EU AI Act framework, and what data protection and security considerations apply. The result is a clear, actionable checklist showing exactly where immediate action is needed and where fine-tuning is sufficient.
Step 2: Selecting the Right AI Tools from the Market
Once it is clear what tasks the organization actually wants to use AI for and at what risk level, the next question is: which specific tools should be used? These are not merely technological, but significant legal and data protection decisions too, especially when the company works with client data, contracts, or other sensitive information.
When selecting tools, it is always necessary to examine how the provider handles data: where processing takes place (within or outside the EU, and which sub-processors are involved), who holds the intellectual property rights over outputs generated by the system, and whether the provider can use inputted data for further model training or other purposes. It also matters whether you are dealing with a public, general-purpose model or a solution developed specifically for enterprise use.
Technical and cybersecurity considerations are equally critical: role-based access control, logging, an admin interface, and ensuring that employees do not work with company data assets through personal accounts. A well-structured AI strategy yields a so-called approved tools list — a concise, clear register of AI tools that have been explicitly vetted and approved by the legal, IT, and compliance teams.
Step 3: AI Use Policy — Establishing Clear, Practical Rules for Use
After the AI Audit and tool selection, you need an internal policy that clearly describes how employees may use AI in their day-to-day work. This document is the AI Use Policy — it also serves as an operational guide that answers the most important questions about AI use. The primary purpose of an AI Use Policy is to establish the conditions under which employees may use AI systems within the organization. This includes, among other things, prohibiting the input of personal data and confidential information, regulating use through company accounts, and clearly defining what constitutes prohibited AI use.
A modern AI Use Policy, however, does not stop at distinguishing permitted from prohibited use. Equally important is the definition of monitoring rules: how the deployment of AI systems can be examined, what data employees are feeding into these systems, and what data protection and information security requirements apply to each solution. Ownership of generated content is another key issue — from both a business and legal standpoint, it matters what rights the organization holds over AI-assisted content and how that content may be used.
An effective AI Use Policy also serves as a tool for building organizational awareness. It is therefore advisable to include a dedicated section on AI training and internal communication, define the consequences of policy violations, and establish a regular review schedule. Care should also be taken to ensure that the AI Use Policy is not an isolated document within the organization, but integrates with existing data protection, cybersecurity, HR, and vendor policies. And when it is supplemented with practical guidance — such as a best practices summary or an internal quick-reference guide — the AI Use Policy truly becomes a functioning, auditable compliance tool embedded in the organization’s everyday practice.
Step 4: AI Training — Making Sure the Rules Work in Practice
Even the best AI strategy and the most professional policy will fail if, in everyday practice, employees do not understand what it is for, why the company is asking what it asks, and what risks they are helping to avoid. Experience shows that the vast majority of AI-related incidents stem not from bad intent, but from a lack of knowledge: someone does not see the problem with copying a contract into a chatbot, or does not verify AI-hallucinated (incorrect) legal references.
This is precisely why AI training and ongoing awareness-building is one of the most important control mechanisms. In practice, this typically means mandatory onboarding training, followed by regular, short, case study-based refresher sessions — especially when a new AI tool is introduced or a significant regulatory change occurs. Such training not only clarifies the basics (what counts as AI, what shadow AI means, what “critical thinking” looks like with AI outputs), but also demonstrates good and bad AI usage practices through concrete, company-specific examples.
A training program will be truly effective if the organization designates AI leads (e.g., a legal director, data protection officer, cybersecurity specialist) to whom employees can bring questions and who can address emerging issues quickly.
How Can We Help?
At SimpLEGAL, we help our clients channel their AI use into a proper framework that minimizes legal risks while supporting adoption.
We conduct AI Audits to examine office AI usage, support the selection of appropriate AI tools, prepare and customize company or office AI use policies in Hungarian or other languages, and develop AI training programs for employees.
This way, the use of artificial intelligence genuinely pays off as a tool that delivers business value — while avoiding the associated risks.
