Cartographier les risques IA, c’est gouverner l’usage avant qu’il ne vous échappe

The deployment of artificial intelligence (AI) in enterprises and administrations is progressing faster than it is being effectively managed. Driven by various departments, experiments with SaaS tools, integrated copilots, or open-source models are proliferating without IT or legal validation. This unchecked expansion exposes organizations to significant risks, including ethical, legal, and operational challenges, which necessitate stringent governance. A pragmatic approach involves establishing an AI risk management framework .

Distributed Usage and Limited Visibility

Currently, an HR manager might integrate an AI assistant into recruitment tools without consulting the IT department, or a marketing director might share customer data with a conversational agent without GDPR impact analysis. An internal audit in a large distribution group revealed 17 unregistered AI use cases , some of which handled sensitive data without encryption.

These initiatives often bypass standard validation processes, accelerating as platforms like Microsoft Copilot, Notion AI, Mistral, and Claude integrate seamlessly into business tools without technical deployment efforts. This lack of transparency poses a primary risk level, as without visibility, control is impossible, increasing the likelihood of incidents such as data breaches, decision-making errors, or violations of industry standards.

To regain control, a structured approach is required, based on four key pillars that, while simple in principle, demand rigorous implementation.

1. Identify Active or Potential AI Use Cases

The first step is to compile a comprehensive inventory of all initiatives, whether in production or testing, internal or external. This includes:

SaaS tools (ChatGPT, Claude, Midjourney...) Relevant business functions (finance, HR, customer support...) Model providers (OpenAI, Hugging Face, Mistral...)

Each use case should be analyzed based on the type of data handled:

Personal data (GDPR) Sensitive data (health, ethnicity) Strategic data (legal documents, patents) Confidential data (roadmaps, source codes)

The goal is to correlate data sensitivity with regulatory requirements and technical protection capabilities (encryption, pseudonymization, auditability).

The complexity goes beyond usage to include the nature of the deployed models :

Open or closed LLMs, fine-tuned or not Degree of autonomy (suggestion, decision, execution) Vulnerability to specific attacks (prompt injection, hallucinations, bias, supplier dependency)

Frameworks like OWASP Top 10 LLM or MITRE ATLAS provide guidance on identifying these vulnerabilities.

This involves structuring a matrix that crosses criticality, probability, impact, and mitigation measures . This management framework must be dynamic, updated throughout projects, and shared with stakeholders.

Use Case Data AI Model Criticality Probability Proposed Control

Email Generation for HR Personal Data GPT-4 via SaaS High Medium Training + Anonymization

Marketing Reporting Internal Data Mistral Open Source Medium Low Secure Local Deployment

Customer Support Agent Client Data Proprietary API High High Logging + Monitoring

Cross-Functional Governance and Compliance

AI risk mapping is no longer the responsibility of a single department and involves the IT department, legal, compliance, cybersecurity, and business units . It is crucial to implement shared governance.

This approach is essential as regulatory obligations tighten:

European AI Act : Impact assessment, traceability, and explainability requirements for certain high-risk use cases. ISO/IEC 42001 : AI system governance standard with documentation, human oversight, and incident management. Sectoral Requirements (banking, health, insurance): Obligations for algorithmic system management.

Checklist for Initiating AI Risk Mapping in Organizations

Have you identified AI projects, including POCs and pilot initiatives? Have you classified data types by sensitivity level? Have you identified vulnerabilities specific to the models used? Do you have a matrix for risks, impacts, and responsibilities? Is governance inclusive of all stakeholders? Is your framework auditable by a regulator or B2B client?

Mapping AI risks provides a sustainable foundation . In an environment where regulators are advancing rapidly, clients are increasingly demanding, and reputations can be affected by poorly calibrated usage, having a clear framework is a strategic necessity. Governing AI starts with knowing its location and utilizing this time to map AI within your organization.

D’après FrenchWeb.