In companies as in administrations, artificial intelligence unfolds faster than it is under control. Supported by professions, experiments are multiplying via SaaS tools, integrated co -pilotes or open source models, without IT or legal validation. This invisible proliferation exposes organizations to major risks (ethical, legal, operational) that only rigorous governance can contain. A pragmatic approach is to establish Risk mapping IA.
Diffuse uses, incomplete visibility
Today, an HR manager can integrate an AI assistant into his recruitment tools without referring to the CIO, a marketing director can entrust customer data to a conversational agent without RGPD impact analysis. In a large distribution group, an internal audit thus uncovered 17 IA use cases not referencedsome of which processed sensitive data without encryption.
These initiatives, often legitimate on the merits, escape the usual validation circuits. The phenomenon accelerates as platforms (Microsoft Copilot, AI concept, Mistral, Claude …) integrate with business tools, without effort of technical deployment.
This opacity is, in itself, a first level of risk, because without visibility, no control is possible. And without control, the probability of incidents increases significantly, whether data leaks, decision -making errors or violation of sectoral standards.
A four -step method
To regain control, a structured approach is essential, it is based on four pillarssimple in their principle but demanding in their implementation.
1. Identify active or latent use cases
First step: draw up a Functional inventory Of all initiatives, whether in production or test, internal or external. This includes:
- SaaS tools (Chatgpt, Claude, Midjourney…),
- the business functions concerned (finance, HR, customer support, etc.),
- Model suppliers (Openai, Hugging Face, Mistral, etc.).
2. Classify manipulated data
Each use case must be analyzed according to the nature of the data processed:
- personal (GDPR),
- sensitive (health, ethnicity),
- strategic (legal documents, patents),
- confidential (roadmaps, source codes).
The goal is to cross the sensitivity of data with regulatory requirements and technical protection capacities (encryption, pseudonymization, auditability).
3. Evaluate the risks linked to models
Complexity does not only reside in use, but in the Even nature of the deployed models ::
- LLM open or closed, fine-tunated or not;
- degree of autonomy (suggestion, decision, execution);
- Exposure to specific attacks (prompt injection, hallucinations, bias, dependence on the supplier).
Standards like OWASP TOP 10 LLM Or Atlas miter allow you to objectify these vulnerabilities.
4. Formalize a risk matrix
It is a question of structuring a matrix crossing criticality, probability, impact and mitigation measures. This steering framework must be alive, updated over the projects, and shared with the stakeholders.
Synthetic matrix example
| Usage case | Data | IA model | Criticality | Probability | Proposed control |
|---|---|---|---|---|---|
| HR email generation | Personal data | GPT-4 via SaaS | High | Average | Training + anonymization |
| Marketing reporting | Internal data | Mistral Open Source | Average | Weak | Secure local deployment |
| Customer support agent | Customer data | Owner API | High | High | Journalization + monitoring |
Transversal governance, proof of conformity
Risk cartography is no longer a single service and involves the CIO, Legal Directorate, Compliance, Cybersecurity and Trades. It is essential to make it shared governance.
This approach becomes essential at a time when regulatory obligations are tightened:
- AI ACT EUROPE : Obligation to evaluate impact, traceability, explanability for certain cases of risk at risk.
- ISO/IEC 42001 : IA systems governance standard with documentation, human supervision, incident management.
- Sectoral requirements (Bank, Health, Insurance): Obligation to pilot algorithmic systems.
Idea of Check-list to initiate an ia cartography in its organization
- Have you identified AI projects, including POCs and trades pilots?
- Have you classified data types by sensitivity level?
- Have you identified the vulnerabilities specific to the models used?
- Do you have a risk / impact / responsibilities matrix?
- Does governance imply all stakeholders?
- Is your device auditable by a B2B regulator or customer?
Cartographing the risks IA is give it a lasting base. In an environment where regulators advance faster than ever, where customers become more demanding, and where reputation can be affected by poorly calibrated use, having a clear framework is a strategic necessity. Govern the AI, it is first of all Know where it is, take advantage of this summer period to carry out the IA map of your business.
