Generative AI creates value — but without guardrails, it also creates major risks
A large majority of French SMBs now use at least one generative AI tool (ChatGPT, Copilot, Midjourney), but only a minority of them have put formalized usage rules in place. The consequences are concrete: many companies have already experienced an incident linked to generative AI (data leak, erroneous content published, copyright issue). The good news: these risks are entirely manageable with a suitable governance framework.
The problem
Generative AI has taken hold in companies at an unprecedented speed. But this rapid adoption has created a gap between actual usage and the guardrails in place. Leaders find themselves facing a dilemma: slow adoption (and lose competitiveness) or let it run (and expose themselves to serious risks).
The concrete risks to which SMBs and mid-market companies are exposed:
- Hallucinations and misinformation: LLMs generate convincing but factually false answers in 3 to 15% of cases. A salesperson who sends a proposal with hallucinated figures or a lawyer who cites a non-existent case can cause significant reputational and financial damage.
- Confidential data leaks: employees enter customer data, contracts, strategic plans into ChatGPT or other public tools. This data can be used to train the models and potentially resurface in other users' answers.
- Bias and discrimination: models reproduce and amplify the biases present in their training data. A CV screening tool can discriminate against certain profiles, a content generator can convey stereotypes.
- Legal risks: intellectual property of generated content, GDPR compliance, liability in case of error. The legal framework is still unclear and the European AI Act imposes new transparency obligations.
The lack of a formal framework exposes the company not only to operational incidents, but also to growing regulatory sanctions. There is an urgent need to move from uncontrolled adoption to managed adoption.
The AI solution
A generative AI governance framework suited to SMBs rests on three complementary pillars. The goal is not to bureaucratize innovation, but to set up intelligent guardrails that protect without slowing things down.
AI usage policy
A clear reference document that defines the authorized, supervised and forbidden uses of generative AI. It covers approved tools, the categories of data never to be entered, the validation processes for generated content, and each employee's responsibilities. This document is the foundation of any AI governance framework. Our AI governance offering includes a ready-to-adapt template.
Anti-risk checklist
A 10-point operational checklist that every employee can apply before using a generative AI tool: checking the data type, choosing the appropriate tool, validation process, traceability of generated content. Easy to implement, it covers 90% of common risks.
Training and awareness
A training program tailored to each profile (management, business, IT) that covers risks, best practices and concrete cases. The training lasts 2 hours and includes practical exercises on detecting hallucinations and handling sensitive data. Discover our AI training.
Implementation
Here are the three steps to deploy a generative AI governance framework in your SMB in less than 30 days, without a specialized consultant or a substantial budget.
Map current usage (week 1)
Send a short questionnaire (10 questions) to all employees to inventory the generative AI tools used, usage frequency, the types of data entered and the use cases. Identify the 3-4 most frequent and most risky uses. This snapshot is essential to calibrate your usage policy.
Draft and roll out the usage policy (weeks 2-3)
Based on the mapping, draft your AI usage policy by distinguishing three levels: free uses (brainstorming, non-sensitive writing), supervised uses (customer documents, financial analyses — mandatory human validation), forbidden uses (personal data, industrial secrets, automatic decisions without supervision). Have it validated by management and the DPO, then roll it out with a 30-minute presentation session.
Train and monitor (week 4 and beyond)
Organize 2-hour training sessions per profile (management, business, IT). Include practical cases: detecting hallucinations, anonymizing data before entry, using RAG to ground answers. Set up an incident reporting channel and a quarterly review of the policy. Measure the adoption rate and the number of incidents to adjust.
Results
Frequently asked questions
What are the main risks of generative AI for an SMB?
The five major risks are: hallucinations (false but convincing answers, a rate of 3 to 15% depending on the models), confidential data leaks (employees entering sensitive information into public tools), generation bias (stereotypes reproduced or amplified), vendor dependency (technological lock-in), and legal risks (copyright, GDPR compliance).
How do you prevent data leaks via ChatGPT or other AI tools?
Three immediate measures: enable the 'do not train on our data' mode in the tools' settings, deploy an enterprise version (ChatGPT Enterprise, Azure OpenAI) with contractual guarantees, and train employees on the categories of data forbidden to enter (personal data, trade secrets, unpublished financial data).
Does generative AI comply with the GDPR?
The use of generative AI can be GDPR-compliant if you take the necessary precautions: do not enter identifiable personal data, use enterprise versions with DPAs (Data Processing Agreements), inform the data subjects if personal data is processed, and document your usage in your processing register.
How do you detect an AI model's hallucinations?
Several complementary approaches: enforce a human verification process (human-in-the-loop) for critical use cases, use RAG (Retrieval Augmented Generation) techniques to ground answers in verified sources, ask the model to cite its sources and verify their existence, and put in place confidence scoring on the generated answers.
For technical profiles
Comparison of generative AI deployment strategies by the level of security required:
| Criterion | Cloud API with DPA (Azure OpenAI) | On-premise open-source model | Consumer SaaS (ChatGPT Plus) |
|---|---|---|---|
| Data confidentiality | High (DPA, encryption) | Maximal (local data) | Low (data held by OpenAI) |
| GDPR compliance | Yes (EU hosting possible) | Yes (full control) | Partial (US transfer) |
| Protection against hallucinations | RAG + content filtering | RAG + custom guardrails | None (raw usage) |
| Monthly cost (50 users) | 500 – 2,000 euros | 800 – 3,000 euros (GPU) | 1,000 euros (20 euros/user) |
| Skills required | API developer | ML Ops + Infra | None |
| Traceability and audit | Full logs via API | Full control | None |
| Recommendation | SMBs with sensitive data | Mid-market with a data team | Individual exploration only |
Recommended architecture for AI governance: deploy Azure OpenAI (or AWS Bedrock) with an enterprise proxy that logs all requests, applies content filters and blocks the sending of sensitive data (via regex or NER). Add a RAG pipeline to ground answers in your internal data and reduce hallucinations by 80%. Infrastructure cost: 300-800 euros/month for 50 users.